In India’s hospitals, data tells very different stories. A Delhi TB clinic may show endless lung scans, while an oncology hospital in Chennai stores tumour-heavy datasets. For artificial intelligence (AI), this abundance is both a blessing and a curse: plenty of data but little coherence.

Patient data is sensitive and very varied across different kinds of hospitals, says Hima Makonahally Pratap, physician advisory board member at the International Journal of Clinical Research. “Each hospital has its own unique set of patients, and data is stored separately, not just for privacy reasons, but because their very nature differs.”

This is where federated learning (FL) comes into play, a framework that enables AI models to learn collaboratively across hospitals, without transferring the underlying patient data. In other words: collaboration without compromise.

The Challenge of Label Skew

One of the biggest hurdles in Indian healthcare data is what researchers call label skew, when disease distributions vary drastically across hospitals.

“When one hospital sees TB patients and another focuses entirely on oncology, you can immediately see how different their data would look,” Pratap notes.

The National TB Prevalence Survey (NPSI) found that TB affects 31% of Indians over 15, with heavy regional variations. This means hospitals in states like Delhi or Tamil Nadu naturally develop specialised datasets. For AI, this causes two main problems:

• Model divergence: Each hospital’s AI model becomes highly specialised, but when aggregated into a global model, the system is pulled in conflicting directions.
  
• Catastrophic forgetting: Knowledge of one disease (like TB) may be overwritten when new data from another speciality (like oncology) is introduced.
  

The result? Instead of converging to a universal solution, AI struggles to serve anyone well.

Yet label skew doesn’t always manifest equally. Dr Zainul Charbiwala, cofounder and CTO of Tricog, a medtech company, observes less skew in cardiac data.

“We currently have about half of our data from urban and the other half from rural healthcare facilities, and we’re not seeing this divide play a big role. The diversity of conditions is so high and the underlying causes are quite similar. The differences don’t stand out too much. In cardiology, ECG is the go-to test everywhere, so the modality is consistent,” he explains.

This nuance highlights a key insight: some medical domains may lend themselves more naturally to federated learning, while others (like radiology) face tougher integration challenges due to differences in equipment, data resolution, and workflows.

From Chaos to Symphony

Pratap uses music to describe the challenge of integrating diverse datasets: A great guitarist and a great pianist may sound wonderful, but if they play at once without coordination, there will only be noise, no music. “Our goal is to preserve their brilliance while creating a symphony.”

Federated learning, combined with smart strategies, aims to create that symphony.

By embedding structured medical knowledge graphs, such as UMLS and SNOMED CT, federated models don’t just learn patterns; they learn relationships. This ensures respiratory conditions, for instance, are weighted more heavily when TB hospitals contribute to lung-related models.

Techniques like FedProx add “gravity” to local models, gently pulling them toward the global model while allowing for speciality-specific variance.

The multilevel aggregation of structured hierarchies in areas of respiratory, cardiovascular, and neurological health, ensures that models evolve within coherent medical contexts before being rolled up into a broader framework.

Tackling the Gaps

One of AI’s biggest limitations in healthcare is the lack of data for rare conditions. Here, synthetic data generation offers a lifeline.

Charbiwala highlights the challenge in cardiology: “Rare conditions are underrepresented. We don’t typically use synthetic data generation from scratch, but instead rely on augmentation, sampling rarer conditions more often and subtly modifying signals to add variation. This avoids bias while still giving the model enough examples to learn from.”

Emerging frameworks like Gen-FedSD can generate realistic medical images based on text prompts, filling critical gaps without exposing patient identities.

India’s healthcare infrastructure is far from uniform. Urban centres boast cutting-edge MRI machines, while rural clinics may rely on older X-ray setups. Network connectivity is another hurdle.

Tricog addresses this with cloud-connected ECG machines. “One element of our design has been to ensure that our devices work even in poor network conditions. We used to have problems circa 2015-16, but with widespread 4G/5G availability, there’s no issue at all today,” says Charbiwala.

For other modalities, federated learning employs hybrid gradient compression (HGC), which smartly reduces the size of updates shared across networks while preserving vital diagnostic signals. This allows even bandwidth-limited rural clinics to participate meaningfully.

Privacy, Regulation, and Trust

Incorporating India’s Digital Information Security in Healthcare Act (DISHA) is central to federated learning adoption.

“We never move raw data, ever. Every model update is auditable, every hospital has full control, and patients have granular consent,” stresses Pratap.

This approach addresses concerns about data misuse, ensuring compliance while fostering public trust.

The potential of FL is evident in India’s healthcare landscape. In Delhi and Bihar, hospitals are using local models to enhance tuberculosis screening and improve pneumonia and COPD detection. Specialised cancer centres in Chennai contribute to global models for early tumour detection without sharing raw scans. Tricog’s ECG platform in Karnataka helps rural clinics identify over 140 cardiac conditions, showcasing FL’s effectiveness in low-resource settings while ensuring data privacy.

The experts agree that India has a unique opportunity to lead. “If we get this right, India could become the blueprint for federated healthcare AI globally. We have diversity, scale, and strong regulatory frameworks. That’s exactly the testbed the world needs,” Pratap reflects.

The key is not to chase glamorous solutions, but to ensure they actually work for the last-mile clinic,” says Charbiwala.

Federated learning is not a silver bullet, but it offers India a pathway to balance privacy, diversity, and innovation in healthcare AI. 

“Each hospital is a brilliant soloist. Federated learning is how we turn them into an orchestra,” concludes Pratap.

The post Orchestrating a Healthcare AI Symphony in India Through Federated Learning  appeared first on Analytics India Magazine.

In the far West, American entrepreneur Bryan Johnson is finding unfathomable ways to prevent himself from dying. He has even built a community called ‘Don’t Die’! While immortality remains out of reach, humans have long pursued ways to extend the lifespan of our race.

In India, we are integrating technology into healthcare to transition from reactive to predictive AI-driven health solutions.

At RISE – Longevity India Conference 2025 that is underway in IISc, Bengaluru, Accel partner and Longevity India co-founder Prashanth Prakash outlined how AI and systems biology are transforming diagnostics, creating a healthcare model focused on prevention rather than cure.

AI for Predictive Health 

Prashanth Prakash at RISE for Healthy Aging Conference. Credit: AIM

Prakash, who supports Biopeak, a longevity clinic focusing on precision diagnostics and AI-driven insights for health management, highlighted how AI is bridging the gap between systems biology and clinical applications. 

Unlike traditional medical diagnostics that rely on isolated tests, AI-driven models analyse molecular pathways, genetic markers, and large-scale health data to predict diseases before symptoms appear.

He stressed that India is uniquely positioned to build a next-gen AI-driven health system, bypassing legacy constraints that have slowed down western healthcare. 

“We don’t have a lot of healthcare infrastructure, which means we have the luxury to engineer something new without being compromised by all kinds of insurance and other entities,” Prakash said at the conference. 

He also explained how AI is unlocking new possibilities. “The problem with the current system, which of course all of us are very familiar with, is that it’s slightly more partitioned and siloed,” he remarked.  

Prakash noted that the real connection is with systems biology, which has matured over time and is now being brought into the mainstream by AI.

He envisions AI playing a key role in quantitative language models that go beyond text-based data processing. These models can interpret complex medical data, genomic sequences, and cellular interactions, making AI a powerful tool in predictive medicine.

“It’s probably generative AI, but I think it’s a web of complex AI systems. You need classical reasoning AI systems, generative AI, and quantitative language models, not just systems that can deal with English, but those that can deal with more complex medical data.”

The shift towards AI-driven diagnostics is already happening through Biopeak. Instead of relying on conventional blood tests, Biopeak leverages AI to uncover early indicators of chronic diseases that might otherwise go undetected. 

“What Biopeak is doing is again very cutting edge because in conscious medicine, there are things that you can do in the US, and there are things that you can do in Singapore, but I think there is a more opportunity here in India to find the standards for conscious medicine,” he said.

Government and Research Support

Institutions such as IISc have taken steps to integrate AI into longevity research. IISc’s ICMR-backed Center for Advanced Research is focusing on computational models for aging and predictive health analytics.

Govindan Rangarajan, director of IISc, reinforced this interdisciplinary approach. “The Center for Advanced Research will focus on healthy aging and also look at all the models for modeling aging and aging axis etc. 

“It involves five departments—besides the biology department, it also includes computer science and materials engineering,” he said at the conference. 

Karnataka’s health minister Dinesh Gundu Rao said, “There is so much happening in the fields of medical science and bioscience on the cellular and molecular levels. It’s not just limited to increasing your life span and reducing or slowing down aging, but also reversing aging.” 

Interestingly, traditional health practices such as Ayurveda are also being studied with respect to biology and genomics to understand healthy aging, as highlighted by Vaidya Rajesh Kotecha, secretary of the Ayush ministry. 

“From the Ayurveda perspective, it is interesting that there is a whole science that talks about the aging population and how to provide healthy aging or quality health for the aging population,” said Kotecha. 

With a number of initiatives driving longevity research, the country is better placed to achieve breakthroughs as opposed to the West where AI adoption in healthcare is hindered by regulatory constraints. Predictive health models can, in fact, become a norm rather than an exception, which will all be powered by tech. 

“Computer science will be the glue that will bind everything together,” Prakash concluded. 

The post How Longevity India is Riding the AI Health Revolution appeared first on Analytics India Magazine.

Critical care in India faces a major crunch, with estimates suggesting only 2.3 ICU beds per 100,000 population. Furthermore, intensivists or critical care doctors are also in short supply, with only 5,000-6,000 trained professionals in our country. This shortage becomes a bigger threat in smaller towns and non-metro regions, leading to unfortunate, preventable deaths. 

A Bengaluru-based healthcare startup, Cloudphysician, aims to address this disparity with AI. 

ICU Care 2.0

Cloudphysician was founded in 2017 by Dileep Raman and Dhruv Joshi, two US board-certified intensivists who have witnessed the healthcare system in the West. They built the platform with a mission to use AI and telemedicine to bridge the skill and resource gap in India’s critical care infrastructure. 

By connecting ICUs through high-quality video and bedside data analytics, Cloudphysician looks to improve patient outcomes in both neonatal and adult critical care. 

“We have approximately 3.5 lakh ICU beds in the country. However, for a country our size, we need between 8 to 10 lakh ICU beds,” said Raman in an exclusive interaction with AIM. 

“It’s not that you put a bed and a ventilator and add some devices, and it becomes an ICU bed. Besides the must-have hardware and the infrastructure, you also need skilled people to run it and a set of processes that make the high-quality ICU function. That’s what makes an ICU bed,” explained Raman.  

Healthcare Operational AI

Cloudphysician uses a combination of multimodal AI models, incorporating inputs from video feeds, lab results, medical records, ambient audio, and established medical guidelines. This integrated approach allows the AI to detect critical issues, such as potential infections or tube disconnections, and provide actionable insights to doctors in real time. 

“So it is not about predicting who is going to get worse or better. It’s more about analysing what exactly is going on because that is what enhances the efficiency of the doctor with us,” explained Raman.

They use a combination of computer vision models for visual analysis and LLMs for reasoning and recommendations. Raman said they also leverage platforms like Google Cloud and OpenAI alongside their in-house models. 

The startup currently covers over 1,500 ICU beds across 200 hospitals in more than 100 cities throughout India and has even demonstrated a significant impact on reducing mortality by up to 47% in certain ICUs. 

A few months ago, the startup raised $10.5 million in a funding round led by PeakXV Partners, Elevar Equity and Panthera Peak. 

Humans in the Loop

The platform extensively employs AI, albeit as an augmenting tool. “The AI is not making any patient care decisions. It’s still the doctor and the nurse, but they’re doing it in a far more efficient manner now,” said Raman. If an ICU doctor can see 8-10 patients, Cloudphysician will be able to increase that by 6-8 times. 

Hands-on clinical training is a requirement for the startup, so half of its workforce consists of clinicians, doctors, and nurses who undergo intense training. Currently, the startup has a 280-member team, and HCG, Motherhood, and Cytecare cancer hospitals are some of its customers.

AI in Healthcare

A number of AI-based health tech startups have emerged recently, with the goal of addressing the critical staff shortage in patient care. The AI in healthcare market is expected to grow to $173.55 billion by 2029. 

Dozee, a Bengaluru-based startup, offers an AI-based contactless remote patient monitoring system. It tracks key metrics such as alert sensitivity, specificity, response time, and healthcare activity. The system is said to have the potential to save 21 lakh lives annually and reduce healthcare costs by INR 6,400 crore.

Cloudphysician has an ambitious vision to become the global engine for delivering healthcare, “Just like what happened in IT services decades ago,” remarked Raman. 

The post This Bengaluru AI Startup Claims to Cut ICU Mortality by 47% appeared first on Analytics India Magazine.

Rising 2024, India’s premier conference on diversity and inclusion in technology, brought together leaders from  the tech industry to discuss and collaborate on innovative strategies for building inclusive workplaces.  

Providence India, one of the event partners at Rising 2024, exemplifies how fostering inclusivity and diversity in  the workplace can drive equity-centered leadership, both within healthcare and beyond.  

Headquartered in Hyderabad, Providence India is the engineering, operations, and innovation hub of Providence,  a not-for-profit health system in the US. Providence operates 51 hospitals and 1,000 clinics across seven western  US states. Founded 170 years ago by the pioneering Sisters of Providence and the Sisters of St. Joseph of  Orange, Providence today is one of the largest health systems in the U.S., committed to providing world-class,  equitable, affordable and accessible healthcare to diverse communities. 

Making Diversity a Priority 

Providence India has made significant strides in promoting gender diversity, with women comprising 38% of its workforce. Shortly after its inception in February 2020, Providence India formed a 12-member diversity, equity and  inclusion (DEI) council comprising of a business leader as its executive sponsor and members from various parts  of the organization, to fully align DEI initiatives to strategic business priorities, and to ensure that goals were  effectively achieved. 

The council has a charter that covers well-being, gender, education and awareness, and hiring and retention, each  owned and run by a dedicated working group. Overall, these initiatives have earned Providence India multiple  industry recognitions. The organization has been benchmarked against some of the best-in-class organizations, making them a preferred employer among prospective talent pool while improving retention significantly. 

One of the many major initiatives of the Council is the creation of the women’s Employee Resource Group aimed  at easing the way for women, by creating empowering spaces for women to thrive in their professional and  personal lives, through best-in-class flexible benefits and workplace opportunities that enable professional  learning, networking and mentoring.  

Well-being: 

Providence India has acquired WELL Health-Safety Rating^TM and the international LEED Gold certification for  meeting best-in-class building standards for physical, occupational, emotional, and mental well-being and for  providing healthy work environment with ergonomic furniture, accessible health services, and biophilic design. Aimed at supporting its workforce physically, emotionally and across all dimensions of well-being, including special  amenities for women, Providence India has built a holistic work environment for employees under its ‘Total Well being’ priority. ‘My Mental Health Matters’, a progressive initiative launched by Providence in 2021 helps support  Providence employees globally with actionable resources, tools and techniques, education and support on mental  health. The initiative includes comprehensive toolkits and an anonymous-by-choice mental health assessment  that helps employees understand areas they are struggling with, reassure them that help is available along with  where and how to seek help.  

Best-in-class Benefits and Perks 

Health insurance plans cover immediate family, in-laws, and parents, supporting women responsible for their  parents’ care. Providence India’s unique Parenthood Made Easy program supports expectant and new parents by  pairing them with experienced parents, who provide advice and guidance on balancing work and family life before  and during their new parenting journey.  

For expectant women and new mothers, Providence India provides dedicated parking spots, accessible seating,  generous childcare reimbursements, maternity benefits, a private mother’s room, footrests on demand, ergonomic  furniture and accessories. 

Events, Networking and Learning 

Events, such as International Women’s Day, Mental Health Awareness Month and Pride Month, play a significant role in  promoting gender equality, reinforcing the importance of gender diversity and allyship. Providence India hosts  quarterly panel discussions called Diversity Dialogues, a unique forum for leaders, external experts and employees to discuss real-life experiences around inclusion.

Initiatives like the Apex Desk Connect, a periodical face to face connect session with the leadership, provide  employees with a platform to speak with Providence India’s country head. These sessions include dedicated  segments for women to freely share their thoughts, promoting transparency and inclusivity. Providence India  mandates Microaggression and Unconscious Bias at Workplace training for all employees to sensitize them to  known and unknown biases that can influence behavior and decision-making, helping create a more empathetic  and less judgmental workplace.

Building healthier communities 

As part of its larger global Mission, Providence India is focused on building healthier communities and working  towards the health and safety of women. In collaboration with the Government of Telangana, the organization  supports two SHE Shuttle buses—an exclusive, free transport service for women professionals in Hyderabad’s IT  corridor, to enhance transit safety and ease-of-access for women. To ensure that every employee feels aligned to  their larger purpose, Providence India provides a personal hygiene and nutrition kit to an adolescent girl in a rural  area of Telangana for every employee who joins. The organization also conducts mental and emotional well-being  workshops and essential project management skills workshops for female students in local government schools. 

Inclusive Hiring 

According to a 2020 report by the United Nations, approximately 43% of STEM graduates in India are women,  but only about 14% of this group holds STEM jobs. To address this disparity, Providence India emphasizes hiring  women engineers across all career levels, including those just entering the workforce, experienced professionals  and those on a career break. A good percentage of the hiring panel comprises women – both for lateral and  campus hiring.  

Providence India’s ‘Thrive return-to-work program’, welcomes qualified women returning from a career break of 1  year or more with upskilling opportunities that make them workforce-ready. As a result, many women, including  those in leadership roles, have resumed their careers successfully in the organization. 

Providence India’s commitment to diversity, inclusion, and employee well-being has been widely recognized. The  organization has received several prestigious accolades, including Zinnov’s Excellence in Employer Branding  (2024), BusinessWorld HR’s Excellence in Employee Experience, Diversity & Inclusion (2023), ETHR World’s  Best Diversity & Inclusion Learning Initiative (2023), and Zinnov’s Emerging Pioneers of Inclusion & Diversity  (2022). 

Future Goals 

In 2025, Providence India aims to expand and enhance its diversity initiatives by expanding focus on the  LGBTQIA+ community. Through events like inviting LGBTQIA+ speakers and hosting interactions with the  community, Providence India is committed to doubling up its efforts for creating a truly inclusive environment. 

Collaborating with Great Places to Work (GPTW), Providence India conducted LGBTQIA+ awareness sessions  for their leadership and established a dedicated employee resource group (ERG) for LGBTQIA+ employees. The  group convenes every month to explore ways to establish a secure and supportive work environment for the  community, including safe zone training for better awareness, and a Pronouns campaign tailored for India to  emphasize the importance of using preferred pronouns.  

Vasudha Bommireddy, VP – Engineering and business sponsor for the DEI Council at Providence India, shares,  “Providence India prides itself on having established a culture of true inclusivity. By fostering an environment  where every individual feels valued and respected, Providence India is not only paving the way for a more equitable  and thriving workplace but also setting a benchmark for others to follow.”

The post Pioneering Healthcare Through Diversity and Equity-Driven Leadership appeared first on Analytics India Magazine.

All India Institute of Medical Sciences (AIIMS) has partnered with Wipro GE Healthcare Pvt Ltd to advance AI in the healthcare sector. As the clinical partner, AIIMS will provide multi-modal clinical inputs and serve as a real-world environment for evaluating, offering feedback, and deploying GE Healthcare’s AI-enabled solutions.

Wipro GE Healthcare, the technology partner in this collaboration, is set to invest $1 million in the initiative. The investment will focus on developing intelligent systems and workflow solutions that address key clinical areas, including cardiology, oncology, and neurology.

“This partnership allows us to leverage AI to improve patient outcomes and streamline clinical workflows,” said a representative from Wipro GE Healthcare. AIIMS’s role ensures that the AI solutions are effectively tested and tailored to meet real-world medical needs.

The joint effort aims to enhance healthcare delivery through precise diagnoses, innovative treatment protocols, and real-time patient data tracking, ultimately transforming patient care and operational efficiency in the medical field.

A joint working committee from both organisations will oversee the collaboration, focusing on clinical research and academic engagement.

“This partnership with Wipro GE Healthcare holds strategic value and is aligned with the national vision of Viksit Bharat through advanced healthcare,” said Dr M Srinivas, director of AIIMS.

In this regard, Parminder Bhatia, chief AI officer at GE HealthCare, also stated that the Indian healthcare industry has experienced rapid growth, driven by supportive government policies, innovative domestic solutions, and the integration of digital technologies and AI. 

He added that GE HealthCare views AI not merely as a tool but as a transformative force to democratise healthcare and enable increased predictiveness, prevention, and precision.

Chaitanya Sarawate, MD of Wipro GE Healthcare South Asia, emphasised, “Wipro GE Healthcare has a proud legacy of innovation in MedTech. The future of healthcare in India will be driven by technology, with AI at the heart of innovation to enable predictive, personalised, and preventive care at scale. With its transformative vision, diverse patient pool, and scale, AIIMS has been at the forefront of redefining care. This collaboration is a strong step forward in developing foundation models for transformative clinical care and applications for India and the world.”

According to The National AI Portal of India (INDIAai), AI technology promises a transformative leap for India’s healthcare system by 2025, potentially boosting the GDP by $25-30 billion. By connecting longitudinal data—such as imaging, lab results, and medical records—and integrating it with analytics and AI, the collaboration aims to significantly enhance diagnosis accuracy, streamline operations, and improve patient care nationwide.
The AI Health Innovations Hub is set to become a cornerstone for technological advancements in healthcare harnessing AI.

The post AIIMS and Wipro GE Healthcare Establish AI Health Innovations Hub appeared first on Analytics India Magazine.

Technology Holdings, an award-winning global boutique investment banking firm dedicated to delivering M&A and capital-raising advisory services to technology services, software, consulting, healthcare life sciences, and business process management companies globally, recently launched its report titled “What Does GenAI REALLY Mean for IT Services, BPO, and Software Companies: A US $549 Billion Opportunity or Threat?”

“As many as 91% of the respondents believe that GenAI will significantly boost employee productivity, and 82% see enhanced customer experiences through GenAI integration,” said Venkatesh Mahale, Senior Research Manager at Technology Holdings, while speaking at Cypher 2024. He added that in the BPO sector, GenAI is expected to have the biggest impact, particularly in areas such as automation and advanced analytics.

Speaking about the impact of generative AI in the IT sector, Sriharsha KV, Associate Director at Technology Holdings, said, “IT services today generate approximately one-and-a-half trillion dollars in revenue, a figure expected to double in the next eight to ten years.”

He added that Accenture, the number one IT services company in the world, has started disclosing GenAI revenues, and their pipeline is already at a half-billion run rate for the year. “The pipeline has scaled from a few hundred million last year to, I would say, 300 to 400%. That makes us strongly believe that GenAI is real.”

He noted that data centre and chip companies are part of the upstream sectors, as they are responsible for creating the generative AI infrastructure. In contrast, IT services companies are downstream but are gaining momentum in automating building processes using GenAI.

Sriharsha stated that generative AI has a notable impact on testing, debugging, DevOps, MLOps, and DataOps.

M&A Trends in IT Services and BPO

The panel at Cypher further discussed the growing trends in mergers and acquisitions (M&A) driven by GenAI. “2023 was a blockbuster year for funding in GenAI, with $20 to $25 billion infused into the sector,” Sriharsha said. This surge in investment has also translated into increased M&A activity, particularly in the IT services and BPO sectors. “We’ve seen numerous acquisitions focused on integrating GenAI capabilities into industry-specific operations,” he added.

Sriharsha explained that in the BPO sector, GenAI is particularly disrupting contact centres. “By automating up to 70% of calls through a combination of chat, email, and voice interactions, companies can operate with fewer agents while maintaining service quality,” he said. This efficiency allows organisations to redirect resources to higher-value tasks, reshaping the way BPOs operate.

Enhancing Healthcare with GenAI

“India has a population of around 1.4 billion, but there is still a dearth of doctors and nurses,” said Anant Kharad, Vice President at TH Healthcare & Life Sciences. He added that generative AI has several use cases in the healthcare industry that can help solve these problems.

“GenAI will analyse my medical records and try to identify the issues I faced in the past and what I’m experiencing now. It will create a summary of all that and then provide it to the nurse for review, who will handle the initial treatment for the outpatient department. The doctor can then take it from there instead of nurses going through tons of paperwork,” he explained.

He said that this not only enhances patient care but also optimises healthcare workflows, allowing medical staff to focus on more complex cases. Moreover, he added that GenAI is playing a vital role in drug discovery and patient care strategies. “It is working with companies that reverse Type 2 diabetes,” Kharad shared. “It has used machine learning to analyse data from thousands of patients, creating effective treatment curricula that can be rolled out globally,” he said.

The Long-Term Implications of Generative AI

As companies navigate the potential disruptions brought on by generative AI, the long-term impacts on business models and service offerings cannot be overlooked. According to Kharad, the need for traditional models, like manual contact centres, is already being questioned in the BPO sector.

“Testing and debugging in IT services are also being challenged,” he said, suggesting that companies must evolve or risk obsolescence. The healthcare sector, however, appears poised for positive disruption through the application of generative AI. Kharad shared specific examples of how AI can enhance efficiency, especially in diagnostics.

“For instance, instead of a radiologist reading 20 reports a day, AI could enable them to process 100 reports,” he explained. This not only increases operational efficiency but also optimises resource allocation in a sector often constrained by staff shortages.

Furthermore, Kharad pointed out that major players like Amazon are already using generative AI to automate prescription orders based on data inputs. “If AI can handle 90% of the workload, it will reduce costs and provide faster service for patients,” he said.

Kharad further elaborated on the healthcare sector’s response to M&A trends, noting that biotech and health-tech companies are at the forefront. “Pharmaceutical companies in India are partnering with start-ups to drive innovation in drug discovery,” he said. 

For those interested in exploring the implications of generative AI further, Technology Holdings has launched a comprehensive report on its impact on IT services, BPOs, and software companies. The report can be accessed here.

The post The Transformative Impact of Generative AI on IT Services, BPO, Software, and Healthcare appeared first on Analytics India Magazine.

Google has unveiled new AI focussed partnerships in India to boost healthcare, sustainability, and agriculture. 

The tech giant’s latest collaborations include a project with Forus Health and AuroLab to expand diabetic retinopathy screenings, a partnership with Saahas Zero Waste to support India’s circular economy, and the release of its Agricultural Landscape Understanding (ALU) API to developers.

These announcements follow the 10th edition of Google for India event, emphasising the company’s commitment to making AI impactful across the country.

“At Google, we’re not just building AI but shaping a future where AI benefits everyone,” said Dr. Manish Gupta, research director of Google DeepMind, speaking at the roundtable at Google’s Research lab in Bangalore. He highlighted Google’s focus on areas such as language understanding, healthcare, and sustainability to address India’s challenges, emphasising how AI is poised to solve some of society’s biggest issues.

For healthcare, Google has licensed its diabetic retinopathy AI model to healthcare providers and health-tech partners Forus Health and AuroLab in India, and Perceptra in Thailand, to support approximately 6 million AI-assisted screenings for diabetic retinopathy’

Sunny Virmani, Group Product Manager for Health AI Research at Google, expressed Google’s commitment to using AI to eradicate preventable blindness. “We’re expanding our efforts with partners like Forus Health, AuroLab, and Perceptra to ensure that timely intervention reaches even remote communities,” said Virmani.

K. Chandrasekhar, CEO of Forus Health, and Mr. R.D. Sriram, Managing Director of AuroLabs, praised the partnership for bringing AI-powered eye care to the forefront. “We’re confident of impacting millions of lives,” said Chandrasekhar.

In sustainability, Google is collaborating with Saahas Zero Waste (SZW) to use its AI model CircularNet for waste management. CircularNet, trained on global datasets, is helping to improve plastic waste sorting and recycling efficiency. In a pilot, SZW saw an 85% accuracy rate in plastic waste detection, predicting a 10-12% improvement in revenue from higher-quality recyclable materials.

“We believe in AI’s power to build a more sustainable future,” said Sujit Sanjeev, Lead for CircularNet at Google. Arun Murugesh of SZW added, “Google’s CircularNet is playing a crucial role in improving resource recovery and supporting India’s circular economy.”

Google has also opened access to its Agricultural Landscape Understanding (ALU) Research API, which combines satellite imagery with AI to provide insights at the farm level. The API aims to help India’s agricultural sector with data-driven decision-making, optimising farm management, and addressing productivity challenges.

Alok Talekar, Engineering Lead at Google DeepMind India, noted that this API, developed in collaboration with Indian researchers and government agencies, could help mitigate the pressures faced by the agricultural sector. “The ALU API, developed through collaborations between Google’s AnthroKrishi team and researchers and state and union governments across India, reflects our commitment to empowering India’s agriculture ecosystem.”

These partnerships reflect Google’s ongoing commitment to harnessing AI for social good in India, with future collaborations expected to further expand the role of AI in driving sustainable growth and innovation across sectors.

The post Google Announces AI Collaborations For Healthcare, Sustainability, and Agriculture in India appeared first on Analytics India Magazine.

Andrew Ng’s AI Fund has announced its first investment in India, backing Gurugram-based AI healthcare startup Jivi. The AI-focused venture capital fund, known for its investments in tech-driven startups, has chosen Jivi as its entry point into India’s rapidly growing AI market.

Jivi, an AI-powered platform, assists healthcare providers by suggesting potential diagnoses, generating health reports, and performing various administrative tasks, according to the fund’s statement.

While the exact amount invested in the startup or the stake purchased was not disclosed, the move underscores AI Fund’s commitment to expanding its portfolio in the healthcare domain.

Ankur Jain, the former chief product officer of BharatPe, left the fintech giant last year to pursue ventures in AI and healthcare. Alongside G V Sanjay Reddy, chairman of Reddy Ventures, he co-founded Jivi.ai, marking Jain’s second entrepreneurial endeavor. 

With a strong background in AI and machine learning, Jain founded Jivi in January 2024. Just months after its inception, the startup announced its first model, Jivi MedX, which outperformed popular models like Google’s Med-PaLM 2 and OpenAI’s GPT-4 on the Open Medical LLM Leaderboard.

In an exclusive interaction with AIM, Jain revealed that the company plans to launch a series of models focussed on healthcare in the coming months.

“While Jivi MedX is a text model, we are working on a series of other models that we internally call a model cluster. For example, there could be a different model that specialises in diabetes, and for ophthalmology, there will be a different model. 

“The next model from Jivi will be a vision model. We are working on a multimodal MedX,” Jain said.

The vision Jain has with his new startup is to build an AI medical companion that the eight billion-strong population in the world can use for free. AI is finding use cases in other domains of healthcare such as drug discovery and genoming.

India’s artificial intelligence sector is projected to more than double by 2027, reaching up to $22 billion, according to a Nasscom-BCG report. The report highlighted that the products and startups category, which Jivi is a part of, is expected to account for up to 17% of the total AI market, alongside financial services.

AI Fund, which has previously invested in companies such as podcast-production platform Podcastle and investment-research application Octagon AI, is backed by prominent investors including Sequoia Capital and Softbank Group.

Ng, a computer scientist and the managing general partner at AI Fund, has an extensive background in AI development, having led projects at Alphabet’s Google and Baidu. Earlier this year, he also joined the board of Amazon.

The healthcare sector in India has increasingly turned to AI to enhance medical diagnostics, streamline administrative processes, and optimise patient care. Jivi’s use of AI reflects these trends, providing healthcare providers with technology-driven solutions for more accurate diagnoses and more efficient workflows.

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According to Elsevier’s report called “Insights 2024: Attitudes toward AI”, only 22% of Indians are leveraging generative AI for work purposes in healthcare and research while 76% plan to use it in the next two to five years.

In comparison to North America (30%), more respondents from APAC, including India, have used AI for work-related purposes (34%).

The study which surveyed nearly 3,000 people globally working in research and healthcare further discovered that while 95% believe that it is a great source of knowledge, 87% think that using generative AI tools can improve work quality.

Recently CP Gurnani, former tech Mahindra chief said AIM MachineCon GCC Summit, “I can only say there is no human being, including you, who is not working on generative AI.”

Can Generative AI Boost Productivity?

Most studies conducted globally have shown that AI, rather generative AI, can improve productivity for employees, allowing them to have time to focus on other areas of work, as well.

A fresh study from Capgemini, also published today, shows that generative AI is expected to play a key role in augmenting the software workforce, assisting in more than 25% of software design, development, and testing work in the next two years.

But this is contrasting to Genpact’s recently published report “The GenAI Countdown” which stated that 52% of respondents expressed concerns that an overemphasis on productivity could lead to a negative impact on employee experiences​​.

Even when AIM reached out to multiple companies to understand how generative AI has boosted its productivity leading to success, such as through faster product deployment, we received no responses.

Challenges Persist

In India, many healthcare companies are actively tapping into the potential of generative AI. From startups like Practo and Healthify to hospital chains like Apollo and Narayana, everyone is exploring this segment.

But in India, as well, as globally, the primary concerns around this technology revolve around misinformation. As per the survey, around 94% of people are concerned about AI being used for misinformation, 86% worry about critical errors or mishaps and 81% fear AI will erode critical thinking.

However, there is a clear need for transparency and reliable sources to build trust in AI tools since AI is expected to increase the volume of scholarly and medical research rapidly. So 71% expect AI tools’ results to be based on high-quality trusted sources.

Mira Murati, the chief technology officer of OpenAI, also acknowledged the same risks and concerns which lead to biases in LLM-based products in a recent interview.

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Partex NV, a pioneer in AI-powered drug asset management, and Yotta Data Services, a hyperscale GPU cloud and data centre service provoder have announced a partnership to revolutionise the healthcare industry through advanced AI technology.

The collaboration will leverage Yotta’s Shakti-Cloud platform and Nvidia H100 GPU computing infrastructure to enhance the efficiency and effectiveness of developing and deploying AI-based applications of healthcare services, particularly in drug discovery and patient care.

As part of the collaboration, Yotta will set up a preview lab and a dedicated GPU POD for Partex, allowing it to offer its innovative solutions to customers worldwide.

Dr. Gunjan Bhardwaj, Co-founder & CEO of Partex, commented, “Our collaboration with Yotta is a landmark step towards integrating AI deeply into healthcare and will help make drug discovery and various other healthcare solutions possible at scale and at low costs by opening up access to specialised AI infrastructure to customers in India and the world.”

Sunil Gupta, Co-founder, MD and CEO of Yotta, added, “This partnership with Partex NV aligns perfectly with our vision of harnessing AI and cloud technology for societal good. We are confident that our combined strengths will lead to breakthroughs in healthcare, significantly benefiting patients and the industry.”

The global AI market for healthcare is expanding swiftly, growing at a compound annual growth rate (CAGR) of 42% from 2021 to 2028 to reach a market size of $120 billion. The partnership aims to democratise AI-based healthcare solutions, making them more accessible to various stakeholders in the healthcare ecosystem.

Yotta’s Shakti-Cloud AI platform includes various IaaS and PaaS services, foundational AI models, and applications that help enterprises create powerful AI tools and products. Yotta is deploying one of the 10 largest supercomputers in the world with its first cluster of 16,000 Nvidia H100 GPUs at its data centres in Navi Mumbai and Greater Noida.

The collaboration aligns with India’s growing embrace of AI technology, anticipated to reach a market size of $14 billion by 2030. Both teams will form a dedicated cross-organisation unit to craft a joint go-to-market partnership model, with Partex contributing its domain expertise in healthcare and Yotta bringing its advanced AI platform capabilities.

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Google Health has collaborated with Apollo Radiology International in India to leverage the former’s AI capabilities to improve early disease detection, especially for tuberculosis (TB), lung cancer, and breast cancer. 

TB is a major health concern in South Asia and Sub-Saharan Africa, and it lacks adequate radiologist interpretation for chest X-ray screenings, leading to delays in treatment and fatal outcomes. So, AI will help interpret these scans for TB signs, addressing the shortage of trained radiologists and inaccessible diagnostic tests in rural areas.

Similarly, lung cancer and breast cancer screenings in India face challenges due to limited expertise and accessibility. AI assists in making screenings more accessible and identifying incidental nodules, potentially improving early detection rates. With breast cancer having a high mortality rate in India and a scarcity of trained radiologists for interpretation, AI offers hope for scaling up mammogram screenings.

Looking ahead, the partnership seeks to expand AI-powered screening initiatives, with Apollo Radiology International aiming to provide three million free screenings over the next decade. The partnership is an effort to democratise access to healthcare services and improve health outcomes for communities across India.

In addition to early disease detection, Google.org supports initiatives like ARMMAN’s mMitra that address maternal and child health challenges. By harnessing AI predictions to deliver targeted preventive care messages via mobile services, mMitra aims to empower new and expectant mothers with essential health information, ultimately contributing to improved health outcomes for mothers and children in India.

Google is clearly all about making an impact and has a greater affinity for life science and healthcare. Recently, Google Research launched a new medical chatbot called AMIE, specialising in expert-level differential diagnosis.

Unlike the big tech’s previous AI model, Med-PaLM 2, which focuses on medical summaries or answering questions, AMIE serves as a diagnostic tool, generating differential diagnoses. AMIE is built on Google’s PaLM and trained on datasets containing medical conclusions, summaries, and actual clinical conversations.

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San Francisco-based AI research startup Together AI has introduced an advanced new biological foundational model called Evo,  to understand and create sequences from DNA, RNA, and proteins. Evo is trained with a large dataset of prokaryotic genomic sequences, covering 2.7 million whole genomes. 

The main goal of Evo is to address the challenges of modelling entire genomes, given their long length and the intricate changes happening at the level of individual building blocks, or nucleotides.

Unlike previous AI models in biology that focused on specific tasks, Evo is designed as a foundational model. It integrates information across long genomic sequences while being sensitive to individual nucleotide changes. To overcome challenges related to long sequences and precise resolution, Evo uses the StripedHyena architecture, employing a hybrid design of rotary attention and hyena operators.

Evo-1 exhibits several notable capabilities, such as predicting essential genes for an organism’s survival based on small DNA mutations without prior training (zero-shot gene essentiality testing). It also excels in predicting functions across DNA, RNA, and proteins, outperforming other models in protein function prediction. Evo goes a step further by generating novel CRISPR systems, showcasing its ability to design complex molecular structures involving proteins and RNA simultaneously.

The model can generate sequences at the scale of entire genomes, up to 650,000 characters, using a single GPU. 

The researchers see Evo as a groundbreaking technology with the potential to accelerate discoveries in various scientific fields, including biology, chemistry, and material science. Its applications extend to practical challenges such as drug discovery, agriculture, and sustainability. 

In December last year, Together AI received a $102.5 million Series A investment from NVIDIA, Kleiner Perkins, and Emergence Capital.

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Apart from finding cure for diseases, AI is now also your buddy who will help you quit smoking. Quitbot is a LLM-based chatbot developed by Fred Hutch Cancer Research Centre with a complimentary mobile application that integrates a scientifically validated smoking cessation program with ChatGPT, providing users with a virtual counsellor at their fingertips.

In the recent The Prompt show by Trevor Noah, behavioural psychologist Jonathan Bricker from University of Washington, explained the need for building such a chatbot to help people quit smoking.

“There are 8 million people in the world who die every single year because of tobacco use,” he explained that nicotine is more addictive than any other drug. Yet, there are not enough medical programmes that help people quit smoking. 

The app delivers step-by-step guidance on the quitting process, equips users with tools to manage smoking urges, offers motivational support, and allows users to pose their own questions, serving as a doctor. 

Users can interact with the chatbot talking about when they are feeling stressed and what triggers them to smoke, and it will learn about your patterns, and it comes back to you later and reminds you why you are quitting. 

Bricker highlights that QuitBot is not just a chatbot to help people quit smoking, but also a tool that can help them break any habit that they are trying to. Serving as a virtual coach, QuitBot collaborates with individuals to establish goals, provide encouragement and assistance as needed, impart coping skills for cravings, and aid in the recovery from setbacks.

With QuitBot, individuals enjoy convenient 24/7 access to cessation support, empowering them to quit smoking with assistance right in the palm of their hands.

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Oracle Corp. announced several enhancements to its healthcare solutions at its flagship event being held in Las Vegas. This includes new cloud-based electronic health record (EHR) capabilities, generative AI services, public Application Programming Interfaces (APIs), and back-office enhancements designed for the healthcare industry.

The new Oracle Health EHR platform will offer a modern interface and intuitive, guided processes that improve patient and provider experiences with easy-to-use, consumer-grade applications. The platform will also provide convenient self-service options that empower patients while reducing provider burden and administrative workloads.

For providers, taking advantage of a host of new features will not only save time, but also increase efficiency. For instance, using generative AI services, providers will be able to create personalised treatment plans based on the patient’s medical history, current condition, and preferences. The platform will also leverage natural language processing and machine learning to extract relevant information from clinical notes and generate accurate documentation and billing codes.

“Our goal is to deliver one of the industry’s best, most functionally rich EHR systems to reduce wasted time, eliminate redundant processes, and add value every step of the way for practitioners and the patients they serve,” said Travis Dalton, executive vice president and general manager of Oracle Health. 

Oracle Health will be making its clinical and financial resources, such as vitals, appointments, and orders available via public Application Programming Interfaces (APIs). These new APIs will enable integration with Oracle’s clinical solutions and allow partners, customers, and third-party vendors to create more advanced customizations, as well as net new experiences and workflows :-

• Generative AI capabilities: Clinical Digital Assistant enables providers to leverage generative AI together with voice commands to reduce manual work. For physicians, the multimodal voice and screen-based assistant participates in appointments using generative AI to automate note taking and to propose context-aware next actions, such as ordering medication or scheduling labs and follow-up appointments. 

• Human resources enhancements: To help healthcare organisations support their complex staffing needs, Oracle added new AI-powered workforce management within Oracle Fusion Cloud HCM. With AI-powered healthcare scheduling and EHR insights, managers can match the best suited workers to the appropriate assignment based on real-time patient and workforce data.

• Finance and supply chain enhancements: Using Oracle’s existing applications, Oracle will enable healthcare organisations to consolidate disconnected systems and automate critical processes while providing the flexibility needed to support new delivery models ranging from tele-health to home- and community-based care. 

Before Oracle, Amazon’s AWS launched HealthScribe and HealthImaging in July to improve the efficiency and accuracy of EHR using generative AI. In April, Microsoft partnered with Epic, America’s largest EHR to enhance medical records and improve patient-doctor interaction using ChatGPT-4. 

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In the exhilarating quest to revolutionise drug discovery, the world of quantum computing emerges as a beacon of hope, promising groundbreaking advancements that could transform the pharmaceutical industry. With the ability to process vast amounts of data in parallel, quantum computing opens up new horizons for simulating intricate biological systems, optimizing drug candidates, and predicting molecular interactions with unparalleled precision. 

Prateek Jain, Lead Researcher and Architect Quantum Computing at Fractal spoke with Analytics India Magazine  to offer his expert insights on the latest advancements in quantum computing, the synergy between artificial intelligence and quantum algorithms, and the transformative impact this fusion could have on drug discovery, ultimately bringing us one step closer to unlocking life-saving therapies in a fraction of the time previously imagined.

How is Quantum Computing Transforming Drug Discovery and the healthcare industry?

Firstly, quantum computing can significantly accelerate the drug discovery process by simulating large molecules and compounds faster than classical computers, leading to quicker development of new drugs. Secondly, it improves drug design accuracy by predicting interactions between drugs and their targets more effectively, resulting in more efficient and safer drugs. Moreover, quantum computers can identify new drug targets that are currently unknown, offering hope for treating previously untreatable diseases through generative quantum AI methods. 

Additionally, quantum computing optimises drug molecule design and predicts clinical trial outcomes, increasing the likelihood of successful drug development. Furthermore, it efficiently simulates molecular interactions at a quantum level, providing a deeper understanding of complex biological molecules and their interactions with drugs. Lastly, quantum algorithms can analyse vast biological datasets, uncovering hidden patterns and relationships to identify new drug targets and disease biomarkers.

When it comes to optimising healthcare operations, streamlining appointment scheduling, inventory management, and resource allocation for increased efficiency can improve the healthcare industry. Notable research initiatives include using QML to diagnose Alzheimer’s disease at the University of Chicago, predict heart attack risk at the Massachusetts Institute of Technology, and optimize resource allocation in hospitals at the University of California, Berkeley. As QML technology advances, we can anticipate even more innovative applications in the healthcare domain.

Could you share your thoughts on the impact and role of quantum computing and quantum neural networks on personalized medicine, from analysing large genomic datasets to tailoring treatment plans for individual patients?

Personalized medicine is a revolutionary approach in the field of medicine, aiming to customize treatments based on an individual’s genetic makeup and unique characteristics. By utilizing quantum computers, researchers can analyze vast genomic datasets, identifying genetic mutations associated with diseases and creating personalized treatment plans. Quantum simulations enable doctors to predict treatment outcomes and potential side effects, leading to more effective and safer therapies. Furthermore, Quantum Generative AI empowers the development of drugs and therapies specifically tailored to each patient’s genetic profile, unlocking the full potential of personalized medicine.

Quantum Neural Networks (QNNs) are a type of quantum algorithm that can be used to analyze genomic data, gene expression profiles, and biomarker discovery. QNNs are able to take advantage of the quantum mechanical properties of nature to perform these tasks much faster and more accurately than classical computers, for example.

• Genomic data analysis: QNNs can be used to analyze large datasets of genomic data much faster and more accurately than classical computers. This can lead to the discovery of new genes, mutations, and other genetic markers that are associated with diseases.
• Gene expression profiling: QNNs can be used to analyze gene expression profiles much faster and more accurately than classical computers. This can lead to the discovery of new genes that are expressed in different ways in different diseases.
• Biomarker discovery: QNNs can be used to discover new biomarkers that can be used to diagnose and track diseases. This can lead to the development of new diagnostic tests and treatments for diseases.

How are quantum simulations aiding researchers in understanding complex biological processes, such as protein folding and cellular interactions?

Quantum simulations are aiding researchers in understanding complex biological processes by providing a more accurate and complete picture of how these processes work. They are able to take advantage of the probabilistic nature of quantum mechanics to simulate these systems more accurately. This has led to a number of breakthroughs in the field of quantum biology, for example:

At Fractal we conducted & published in IEEE similar research wherein even the smallest of the Quantum processor shows comparable results to SOTA Alphafold for protein fold prediction.

Quantum simulations can be used to identify new drug targets by simulating the interactions between drugs and proteins. This can lead to the discovery of new potential treatments for diseases such as cancer and Alzheimer’s. Our team at Fractal created a Hybrid Quantum Generative AI model to produce novel drug like molecules and it performs better than the classical model

Quantum simulations can be used to simulate the interactions between cells and their environment. This has led to a better understanding of how cells function and how they interact with each other.

Could you delve into the realm of quantum cryptography and its potential to safeguard sensitive patient information from cyber threats?

One of the key features of quantum cryptography is that it is immune to eavesdropping. This is because any attempt to eavesdrop on a quantum-encrypted communication will inevitably be detected, alerting the communicating parties to the presence of an intruder. This is due to the fact that quantum mechanics prevents the measurement of certain properties of a quantum particle, such as its position and momentum, without destroying the particle’s state.

As a result, quantum cryptography offers a much higher level of security than traditional encryption methods. This makes it an ideal solution for safeguarding sensitive information, such as patient medical records. There are a number of different quantum cryptography protocols that have been developed. One of the most well-known protocols is quantum key distribution (QKD). 

In QKD, two parties (usually referred to as Alice and Bob) use a series of entangled qubits to create a shared secret key. This key can then be used to encrypt and decrypt messages, ensuring that only the intended recipient can read the message. QKD has been demonstrated over a variety of distances, including over long-distance fiber optic networks. This makes it a viable solution for safeguarding sensitive information that is transmitted over the internet.

Could you help explore the development of quantum-enhanced sensors for medical imaging applications, potentially revolutionizing MRI, PET scans, and other diagnostic techniques?

Quantum-enhanced sensors for medical imaging applications have the potential to revolutionize MRI, PET scans, and other diagnostic techniques. For example, quantum sensors can be used to improve the resolution of MRI scans. This is because quantum sensors are more sensitive to magnetic fields than classical sensors. This could lead to the development of MRI scans that can see inside smaller structures, such as individual cells.

Furthermore, quantum sensors can be used to improve the accuracy of PET scans. This is because quantum sensors are more sensitive to the emission of positrons than classical sensors. This could lead to the development of PET scans that can detect smaller amounts of radioactive tracers, which would make them more sensitive to diseases. These sensors can also be used to improve the performance of other diagnostic techniques, such as ultrasound and optical imaging.

Challenges and Limitations: What’s Hindering the Widespread Adoption of Quantum Computing in Life Sciences and mainstream healthcare? 

Quantum computing is still in its early stages of development, and there are a number of challenges and limitations that need to be addressed before it can be widely adopted in life sciences and mainstream healthcare.

• The difficulty of building and operating quantum computers. Quantum computers are extremely complex devices, and they are difficult to build and operate. This is due to the fact that quantum mechanics is a very delicate science and is at sub atomic scale.
• The lack of mature quantum algorithms. There are a number of quantum algorithms that have been developed, but many of them are not yet mature enough to be used in real-world applications. This is because quantum algorithms are often very complex, and they can be difficult to implement.
• The lack of data. In order to train quantum algorithms, large amounts of data are needed. However, in life sciences and healthcare, there is often a lack of data that is suitable for quantum computing. This is because many of the data sets that are used in life sciences and healthcare are not structured in a way that is compatible with quantum computing.
• Quantum decoherence: Quantum decoherence is the process by which quantum systems lose their quantum properties due to interaction with the environment. This is a major challenge for quantum computing, as it can lead to errors in the computation.

• The scalability of quantum computers: Quantum computers are still very small, and it is not yet clear how to scale them up to the size that would be needed for practical applications.
• The cost of quantum computers: Quantum computers are very expensive to build and operate. This is a major barrier to their widespread adoption.

What are your thoughts on the ethical considerations surrounding quantum computing applications in healthcare, including privacy, data ownership, and accessibility?

Quantum computing holds immense promise for revolutionising healthcare, but it also introduces ethical considerations that require careful attention. Key concerns include privacy, as quantum computers could potentially break current encryption standards, leading to unauthorised access to sensitive patient data. The ownership and use of vast patient data collected and analysed using quantum computing also raise questions about data ownership and ethical utilisation.

Another significant ethical concern is accessibility, as the current early-stage development of quantum computing may result in disparities in access to healthcare services. Public discussions are essential to fully comprehend the ethical implications of quantum computing in healthcare, ensuring its safe and responsible implementation. 

Additionally, further ethical considerations include the potential for new forms of discrimination based on genetic traits, heightened security risks with the emergence of quantum-powered cyberattacks, and the environmental impact due to the significant energy consumption of quantum computers.

The post Quantum Computing: A Game-Changer in Healthcare and Life Sciences appeared first on Analytics India Magazine.

Google Health, Google Deepmind and Google AI have unveiled Med-PaLM M, a large multimodal generative model that flexibly encodes and interprets biomedical data.  It can handle various types of medical data, including clinical language, medical images, and genomics, and performs well on a wide range of tasks, all using the same set of model weights.

It was built by fine tuning and aligning PaLM-E, a language model from Google AI, to the medical field using a specially curated open-source benchmark called MultiMedBench. MultiMedBench consists of 7 biomedical data types and 14 diverse tasks, such as medical question-answering, generating radiology reports, and identifying genomic variations. With over 1 million samples, this benchmark encourages the development of generalist biomedical AI systems.

Med-PaLM M excels in all tasks on MultiMedBench, often outperforming specialist models by a significant margin and even surpassing PaLM-E, proving the importance of adapting the model to biomedical data.

The key idea behind building a large-scale biomedical AI is to use language as a common framework for different tasks. This allows the AI to combine knowledge from various sources and transfer skills across tasks more effectively.

Excitingly, preliminary evidence suggests that Med-PaLM M can generalise to new medical tasks and concepts and perform multimodal reasoning without specific training. It can accurately identify and describe medical conditions in images using only language-based instructions and prompts, even if it has never seen such cases before.

To assess the practical use of Med-PaLM M in clinical settings, radiologists evaluated AI-generated reports at different scales. The AI’s error rate was found to be comparable to that of radiologists from previous studies, indicating its potential clinical usefulness. The big tech launched the first version of MedPaLM in December, 2022. 

Read more: Responsible AI Takes Center Stage at Google I/O Connect

Google’s Unwavering Commitment to AI in Healthcare

Google’s Med-PaLM 2, a medical chatbot that answers medical questions, has been a fan favourite since its launch. 

Med-PaLM 2 is built upon Google’s language model, PaLM 2, and uses LLMs tailored to the medical domain. The AI has demonstrated impressive performance on medical question-answering datasets, achieving high accuracy on the US Medical Licensing Examination (USMLE)-style questions and the Indian AIIMS and NEET medical examination questions.

Google acknowledges the complexity of personalised medical care and recognises that Med-PaLM 2’s results may not be generalisable to every medical question-answering setting and audience. The AI is trained on medical Q/A datasets but excludes patients’ personal data to adhere to ethical norms.

While having access to patients’ personal data could enhance Med-PaLM 2’s efficiency, privacy concerns are likely to prevent many patients from sharing such information. Google ensures that customers testing Med-PaLM 2 will retain control of their data in encrypted settings, inaccessible to the tech company, and the AI program will not ingest any of that data.

Read more: Google Takes AI Healthcare in Its ‘Med PaLM’

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After OpenAI’s ChatGPT recently cleared all three parts of the USMLE in a single go, as per the results of a new experiment, San Francisco-based medical knowledge management platform, Glass Health recently launched Glass AI, an LLM-based tool capable of generating a diagnosis or clinical plan based on symptoms.

The tool is expected to aid clinicians in developing better diagnoses and clinical plans.

Founded in 2021, Glass Health was started by Dereck Paul and Graham Ramsey. It is a medical knowledge management platform that helps physicians learn medicine faster and leverage their knowledge to provide better patient care. 

Inside Glass AI 

At present, Glass AI is an experimental feature that aims to help clinicians generate a differential (DDx) and draft a clinical plan. The tool is being developed for a clinical audience and is not a search tool for a general audience that some Twitter users perceived.

In just two days of its beta launch, over 14k people used Glass AI to submit 25.7K queries. The co-founder, Paul, said that users found close to 84 percent of differential diagnosis (DDx) and  78 percent of clinical plan outputs as helpful. The post is based on feedback received from users. Paul adds that accuracy ratings are lower. For example, users rate 71 percent of DDx outputs and 68 percent of Clinical Plans as accurate.

He explains that even without perfect accuracy, some outputs from the tool are helpful by suggesting a DDx a clinician didn’t consider or drafting a plan a clinician can easily edit.

The tool sometimes returns output that needs to be more accurate and helpful. However, according to Paul, these limitations are expected so early in this exploration, especially with more complex entries.

Being aware of the potential for AI to perpetuate harmful biases or stigma from either user inputs or training data, Glass AI has deployed additional safeguards to protect against this. This includes biased human decisions or reflect historical or social inequities, even if sensitive variables, including gender, race, or sexual orientations.

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With the release of large language models like GPT-3 and PaLM, big techs have been experimenting with large language models for quite some time now. Recently, Google also joined the party in response to Open AI’s ChatGPT, called the MedPaLM, specifically for answering medical queries. 

Introducing MedPaLM

While ChatGPT seems to be all over the place with no real use cases, Google Research and DeepMind recently introduced MedPaLM, an open-sourced large language model for medical purposes. It is benchmarked on MultiMedQA, a newly introduced open-source medical question-answering benchmark. It combines HealthSearchQA, a new free-response dataset of medical questions sought online, with six existing open-question answering datasets covering professional medical exams, research, and consumer queries. The benchmark also incorporates methodology for evaluating human model responses along several axes, including factuality, precision, potential harm, and bias.

MedPaLM provides datasets for multiple-choice questions and for longer responses to questions posed by medical professionals and non-professionals. These comprise the clinical topics datasets for MedQA, MedMCQA, PubMedQA, LiveQA, MedicationQA, and MMLU. In addition, a new dataset of curated, frequently searched medical inquiries called HealthSearchQA was added to improve MultiMedQA. 

The HealthsearchQA dataset, which consists of 3375 frequently asked consumer questions, was curated using seed medical diagnoses and their related symptoms. All users who entered the seed phrases were shown the publicly available frequently asked questions that were retrieved using the seed data and created by a search engine.

PaLM to the Rescue 

The researchers developed this model on PaLM, a 540 billion parameter LLM, and its instruction-tuned variation Flan-PaLM to evaluate LLMs using MultiMedQA. 

Flan-PaLM achieves SOTA performance on MedQA, MedMCQA, PubMedQA, and MMLU clinical topics by combining few-shot, chain-of-thought (CoT), and self-consistency prompting techniques, frequently surpassing many strong LLM baselines by a large margin. FLAN-PaLM performs over 17% better on the MedQA dataset of USMLE questions than the prior SOTA. Human evaluation, though, identifies significant gaps in Flan-PaLM responses.

The resulting model that addresses this issue is Med-PaLM, which claims to perform well compared to Flan-PaLM but still needs to outperform a human medical expert’s judgment. 

For instance, a group of doctors determined that 92.6% of the Med-PaLM responses were on par with the clinician-generated answers (92.9%), whereas just 61.9% of the long-form Flan-PaLM answers were deemed to be in line with the scientific agreement. Furthermore, like Flan-PaLM, 5.8% of Med-PaLM answers were assessed as potentially contributing to negative consequences, comparable to clinician-generated answers (6.5%), while 29.7% of Flan-PaLM answers were.

Check out the full paper here. 

Google’s Healthcare Play

In the Google for India 2022 event, Google announced a collaboration with Apollo Hospitals in India to improve the use of deep learning models in x-rays and other diagnostic purposes. Google’s other health partnerships include Aravind Eye Care System, Ascension, Mayo Clinic, Rajavithi Hospital, Northwestern Medicine, Sankara Nethralaya, and Stanford Medicine, among others.

Google isn’t the first tech behemoth to venture into the AI-driven healthcare solution. Microsoft is also working closely with the OpenAI team to employ GPT-3 to facilitate collaboration between employees and clinicians and improve healthcare teams’ efficiency. 

In November 2022, Meta AI also introduced Galactica, the AI-generated programme that claimed it would support academic researchers by generating comprehensive literature reviews and Wiki entries on any subject; however, it failed due to unreliable results. 
Around the same time, Meta AI released CICERO by merging natural language processing and strategic reasoning. It is the first AI agent to perform at a human level in the complex natural language game “Diplomacy.” Playing against humans on the website, the AI agent showed off this SOTA performance by exceeding all other players’ average scores by more than two to one. Additionally, it was among the top 10% of players who participated in multiple games.

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AI-powered multilingual virtual physician, Zini.ai — a flagship product by Grainpad Pvt Ltd, has been selected for the Startup Punjab seed grant award. 

Zini.ai was shortlisted from a cohort of  15 startups that were further shortlisted from more than 150 companies across the country.

“We are honoured to be selected for the Startup Punjab seed grant award. Through our flagship product Zini.ai, our aim is to make expert medical advice easily accessible to everyone and to bridge the gap in the doctor-patient ratio in India. This recognition further strengthens our commitment to build innovative products that have the ability to solve real-world problems,” said Dr Rohit Sharma, CEO and Founder, Grainpad.

The startups selected for the grant will have access to mentors, networking opportunities, interactions with Invest Punjab and other stakeholders. The selected startups were reviewed by prominent institutions like the Institute of Nanoscience and Technology, an autonomous research institute of the Dept. of Science & Technology, Govt. of India. 

Founded in 2017, Zini provides genuine expert medical advice and directs patients to seek timely medical help. Accessible through an app, Zini allows users to ‘Talk to Zini’ about any medical symptom or health information. With an Alexa-like experience, the app can evaluate 950+ health symptoms, 300+ diseases,  provide a detailed report,  recommend the best course of action and share details of nearby medical facilities that a patient can reach out to. 

Zini has been trained on medical data specific to the Indian population. Over the last few years, Zini’s medical team of 15 doctors has curated a database of symptoms, the reasons behind them, and the possible course of action for the symptoms. One can download the app here.

In March 2021, the startup won a grant of 25 Lakhs under the Startup India – NGIS (Next Generation Incubation Scheme) started by the STPI (Software Technology Park of India). 

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Healthcare is under the microscope this year like never before. Hospitals are being asked to do more with less, and researchers are working around the clock to answer pressing questions.

NVIDIA’s GPU Technology Conference brings everything you need to know about the future of AI and HPC in healthcare together in one place.

Innovators across healthcare will come together at the event to share how they are using AI and GPUs to supercharge their medical devices and biomedical research.

Scores of on-demand talks and hands-on training sessions will focus on AI in medical imaging, genomics, drug discovery, medical instruments and smart hospitals.

And advancements powered by GPU acceleration in fields such as imaging, genomics and drug discovery, which are playing a vital role in COVID-19 research, will take center stage at the conference.

There are over 120 healthcare sessions taking place at GTC, which will feature amazing demos, hands-on training, breakthrough research and more from October 5-9.

Turning Months into Minutes for Drug Discovery

AI and HPC are improving speed, accuracy and scalability for drug discovery. Companies and researchers are turning to AI to enhance current methods in the field. Molecular simulation like docking, free energy perturbation (FEP) and molecular dynamics requires a huge amount of computing power. At every phase of drug discovery, researchers are incorporating AI methods to accelerate the process.

Here are some drug discovery sessions you won’t want to miss:

• Powering Computational Drug Design with NVIDIA GPUs
• The Summit Supercomputer as a Virtual Drug Discovery Lab for COVID-19
• Revealing Hidden Determinants of Molecular Recognition in Hit-to-lead Optimization with Graph Convolutional Networks

Architecting the Next Generation of Hospitals

AI can greatly improve hospital efficiency and prevent costs from ballooning. Autonomous robots can help with surgeries, deliver blankets to patients’ rooms and perform automatic check-ins. AI systems can search patient records, monitor blood pressure and oxygen saturation levels, flag thoracic radiology images that show pneumonia, take patient temperatures and notify staff immediately of changes.

Here are some sessions on smart hospitals you won’t want to miss:

• Improve Patient Care with Everyday Sensors and Multimodal AI 
• Architecting AI-Powered Medical Instruments
• Combining Design and GPUs to Improve Surgical Performance

Training AI for Medical Imaging

AI models are being developed at a rapid pace to optimize medical imaging analysis for both radiology and pathology. Get exposure to cutting-edge use cases for AI in medical imaging and how developers can use the NVIDIA Clara Imaging application framework to deploy their own AI applications.

Building robust AI requires massive amounts of data. In the past, hospitals and medical institutions have struggled to share and combine their local knowledge without compromising patient privacy, but federated learning is making this possible. The learning paradigm enables different clients to securely collaborate, train and contribute to a global model. Register for this session to learn more about federated learning and its use on AI COVID-19 model development from a panel of experts.

Must-see medical imaging sessions include:

• Bringing AI to Hospitals: How to Design a Clinical Imaging Infrastructure for AI at the Edge
• The National Swedish Platform for AI Research in Medical Imaging
• XNAT and NVIDIA Clara In Practice: Building a Medical Imaging Research Ecosystem

Accelerating Genomic Analysis

Genomic data is foundational in making precision medicine a reality. As next-generation sequencing becomes more routine, large genomic datasets are becoming more prevalent. Transforming the sequencing data into genetic information is just the first step in a complicated, data-intensive workflow. With high performance computing, genomic analysis is being streamlined and accelerated to enable novel discoveries about the human genome.

Genomic sessions you won’t want to miss include:

• Accelerating Whole Genome Analysis to Make the Most of Your Sequencing Data
• Real-Time Sequencing Meets High Performance Compute

The Best of MICCAI at GTC

This year’s GTC is also bringing to attendees the best of MICCAI, a conference focused on cutting-edge deep learning medical imaging research. Developers will have the opportunity to dive into the papers presented, connect with the researchers at a variety of networking opportunities, and watch on-demand trainings from the first ever MONAI Bootcamp hosted at MICCAI.

Game-Changing Healthcare Startups

Over 70 healthcare AI startups from the NVIDIA Inception program will showcase their latest breakthroughs at GTC. Get inspired by the AI- and HPC-powered technologies these startups are developing for personalized medicine and next-generation clinics.

Here are some Inception member-led talks not to miss:

• Distributed Learning on Sensitive Data
• Cutting-Edge AI Tools Aid in Safe Patient Care in a Smart Hospital
• A Universal, Fast, Auto-Focusing System for Whole Slide Imaging Using Deep Learning
  

Make New Connections, Share Ideas

GTC will have new ways to connect with fellow attendees who are blazing the trail for healthcare and biomedical innovation. Join a Dinner with Strangers conversation to network with peers on topics spanning drug discovery, medical imaging, genomics and intelligent instrument development. Or, book a Braindate to have a knowledge-sharing conversation on a topic of your choice with a small group or one-on-one.

Learn more about networking opportunities at GTC.

Brilliant Minds Never Turn Off

GTC will showcase the hard work and groundbreaking discoveries of developers, researchers, engineers, business leaders and technologists from around the world. Nowhere else can you access five days of continuous programming with regionally tailored content. This international event will unveil the future of healthcare technology, all in one place.

Check out the full healthcare session lineup at GTC, including talks from over 80 startups using AI to transform healthcare, and register for the event today. It is free for individuals from Government, Academia and Non Profit organisations.

The post Top Healthcare Innovators Share AI Developments at GTC appeared first on Analytics India Magazine.

Artificial intelligence (AI) has been integrating remarkable developments in almost every sector, and the healthcare industry is no different. There is an unprecedented amount of data flowing in the healthcare industry, which remains unlabeled and inconsistent, which can be analyzed to extract valuable insights using AI. The technology is also being used to provide virtual therapy and even used in some surgeries. A new paper is published every few weeks that tries to develop better algorithms to enhance the ways in which this industry can leverage the power of AI.

In this article, we will have a look at some of the top AI algorithms that are currently being used in the healthcare industry to solve numerous problems:

1. Support Vector Machines

Support Vector Machines are the most standard machine learning algorithm that is being used by the healthcare industry. It uses a supervised learning model for classification, regression, and detection of outlines. In recent years, the algorithm has been used to predict the medication adherence of heart patients that has helped millions avoid serious consequences, such as hospital readmission and even death. It is also being used for protein classification, image segregation, and text categorization.

2. Artificial Neural Networks

It is a group of deep learning algorithms inspired by the neuron organization in animal brains that can receive signals from a previous layer and send it to the next layer. A network that can learn by analyzing examples or without any human intervention. From pathologists using it for diagnosis to biochemical analysis, an artificial neural network has a bunch of other uses as well. It is further divided into two parts – convolutional neural network (CNN) and a recurrent neural network (RNN). 

Imaging is an important aspect of medical science since it can allow a doctor to know about a disease even before the symptoms arise. Due to this, there are several screening procedures such as Pap smears, Mammograms, Colonoscopy, etc. CNN has proved to be crucial in this segment, as the algorithm is well suited for a multi-class classification problem and binary classification. On the other hand, RNN has proved to be significant when used for pattern recognition in medical time-series data analysis. 

3. Logistic Regression

This machine-learning algorithm is used to predict the current scenario of the categorical dependent variable through the use of predictor variables. It is often used for classifying and predicting the probability of an event, such as disease risk management, which assists doctors in making critical medical decisions. It also helps medical institutions target patients with more risk and curate behavioral health plans to improve their daily health habits. 

4. Random Forest

The algorithm is used to construct multiple training trees at training time for performing classification and regression, and also helps overcome the problem of decision trees’ overfitting. Based on a patient’s medical history, Random Forest is used to predicting the risk of disease and for ECG and MRI analysis. 

5. Discriminant Analysis

Discriminant Analysis is a machine learning algorithm that is used to analyze the adequacy of object classification, and also for assigning one object to single or many groups. From the early diagnosis of diabetic Peripheral Neuropathy to refining the diagnostic features of blood vessel images, discriminant analysis is applied in the healthcare industry. It is also used for electronic health record management systems and to detect signs of mental health disorientation. 

6. Naïve Bayes

Based on the Bayes theorem, this is one of the most efficient machine learning algorithms ever known to mankind and is highly used by the healthcare industry for medical data clarification and disease prediction. When it comes to data mining, classification can be termed as data analysis, which is often used to extract models describing data classes. Since the probability distribution is high, Bayes classifier can achieve an optimal result.

The post Top 6 AI Algorithms In Healthcare 2024 appeared first on Analytics India Magazine.

Artificial Intelligence plays an important role in the pharmaceutical industry and the coming years there is simply no sign of the adoption of this cutting-edge technology slowing down. From making healthcare process automated to help in drug discovery, AI with machine learning can bring revolution in this industry. The key customer-oriented areas where AI is being implemented within the sector are the following:

1. Hands-On Speech Recognition System to analyse customer’s feedback.

Through natural language processing, audio and video files are transcribed from voice to text. These files shall be obtained from video-recordings from patients and customers speaking providing their opinion about a particular product or service. The dataset must be considerably large – more than 300 audio-video files – in order to assure accuracy. The larger the amount of datapoints, the better results that will be obtained.
Within that process, an intelligent platform performs a “sentiment analysis”, which means the platform mines for a series of keywords or statements, as well as the demographics of the speaker (including gender and, possibly, age).
Post-transcription, that data is categorized and classified, ready for analysis based on the chosen parameters.

Machine Learning uses diverse approaches to the creation of autonomous and supervised Neural Network-based speech recognition and translation systems. The two vanguard approaches in this period are Long Short-Term Memory and CNN. The LTSM network or RNN has an 82 per cent accuracy score, while the vision-based Convolutional Neural Network scores 95 per cent accuracy.

2. Hands-On Facial Recognition System and Emotion Analyser (Feedback/Review-oriented).

Every Machine Learning algorithm takes a dataset as input and learns from this data. The algorithm goes through the data and identifies patterns in the data. For instance, suppose we wish to identify whose face is present in a given image, there are multiple things we can look at as a pattern:

1. Height/width of the face.
2. Height and width may not be reliable since the image could be rescaled to a smaller face. However, even after rescaling, what remains unchanged are the ratios – the ratio of height of the face to the width of the face won’t change.
3. Colour of the face.
4. Width of other parts of the face like lips, nose, etc.

Clearly, there is a pattern here – different faces have different dimensions like the ones above. Similar faces have similar dimensions. The challenging part is to convert a particular face into numbers – Machine Learning algorithms only understand numbers. This numerical representation of a “face” (or an element in the training set) is termed as a feature vector. A feature vector comprises of various numbers in a specific order.

As a simple example, we can map a “face” into a feature vector which can comprise various features such as:

1. Height of face (cm)
2. Width of face (cm)
3. Average colour of face (R, G, B)
4. Width of lips (cm)
5. Height of nose (cm)

Essentially, given an image, we can map out various features and convert it into a feature vector as:

So, our image is now a vector that could be represented as (23.1, 15.8, 255, 224, 189, 5.2, 4.4). Of course there could be countless other features that could be derived from the image (for instance, hair colour, facial hair, spectacles, etc). However, for the example, let us consider just these 5 simple features.

Machine Learning can help us here with 2 key elements:

1. Deriving the feature vector: it is difficult to manually list down all of the features because there are just so many. A Machine Learning algorithm can intelligently label out many of such features. For instance, a complex features could be: ratio of height of nose and width of forehead. Now it will be quite difficult for a human to list down all such “second order” features.
2. Matching algorithms: Once the feature vectors have been obtained, a Machine Learning algorithm needs to match a new image with the set of feature vectors present in the corpus.

3. Travel Time and Route Optimization with Machine Learning.

What is the relationship between machine learning and optimization? On the one hand, mathematical optimization is used in machine learning during model training, when we are trying to minimize the cost of errors between our model and our data points. On the other hand, what happens when machine learning is used to solve optimization problems?

In simple terms, we can use the power of machine learning to forecast travel times between each two locations and use the genetic algorithm to find the best travel itinerary for our delivery truck. The following parameters need to be followed:

1. All orders need to be delivered on time.
2. Ensure drivers are not rushed to make it on time by using buffer times and real-time distance.
3. Save fuel by reducing the distance driven.
4. Minimise idle time for drivers — no one likes waiting with a trunk full of packages.
5. Improve vehicle utilisation.
6. Fully automate the process.
7. The algorithm needs to be able to grow with us — supporting different types of deliveries, vehicles and countries.

4. Employee Churn/ Attrition/ Turnover Model with Machine Learning.

Here, you can predict who, and when an employee will terminate the service. Employee churn is expensive, and incremental improvements will give significant results. It will help us in designing better retention plans and improving employee satisfaction. This will be measured through the following attributes:

1. Satisfaction level: It is employee satisfaction point, which ranges from 0-1.
2. Last evaluation: It is evaluated performance by the employer, which also ranges from 0-1.
3. Number of projects: How many numbers of projects assigned to an employee?
4. Average monthly hours worked: How many average numbers of hours worked by an employee in a month?
5. Time spent at the company: it means employee experience. The number of years spent by an employee in the company.
6. Work accident/s: Whether an employee has had a work accident or not.
7. Promotion in the last 5years: Whether an employee has had a promotion in the last 5 years or not.
8. Departments: Employee’s working department/division.
9. Salary: Salary level of the employee such as low, medium and high.
10. Left: Whether the employee has left the company earlier or not.

5. Retail Price Recommendation System with Machine Learning.

We will build a model that automatically suggests the right product prices. We are provided of the following information:

1. Product id: the id of the listing
2. Name:  the title of the listing
3. Item condition_id:  the condition of the items provided by the sellers
4. Category name:  category of the listing
5. Brand name: the name of the brand
6. Price: the price that the item was sold for. This is target variable that we will predict
7. Shipping: 1 if shipping fee is paid by seller and 0 by buyer
8. Item description:  the full description of the item

The post The Future of Healthcare And AI Impact appeared first on Analytics India Magazine.

The outbreak of the COVID-19 pandemic has pushed many essential workers, especially those in healthcare, to take unprecedented steps to protect public health. These incredible efforts have strained the resources and supplies these ‘Healthcare Heroes’ need in order to treat affected individuals.

All the countries around the world are currently fighting the disease, and therefore it is critically vital for hospitals to have an abundant supply of personal protection equipment (PPE) to ensure doctors, nurses, and other healthcare staff can safely continue to work without risk to their health.

At such an unsettling situation of virus outbreak is, it has been inspirational to see the Pune community come together and fight it. Heeding Prime Minister Modi’s call to celebrate the contributions of essential workers, Icertis began asking around to find out how to celebrate and support these vital members of the community. The company realised that early assistance would have the most impact, and therefore decided to ensure healthcare providers have access to PPE throughout the crisis will be essential to Pune in order to win the battle against COVID-19.

Every day, Icertians gather to share a community meal, which is fully funded by the company. But with their recent decision of asking employees to work from home, they thought “why not make these meal-related funds available to support our broader community?” Therefore, in partnership with the Pune Municipal Corporation (PMC), Icertis decided to donate the four-weeks of lunch money, a total of ₹25 lakh, to ensure the safety of doctors, nurses and staff at Naidu Hospital. I am also thankful that the PMC leadership responded quickly and directed our efforts to areas with the most impact.

Naidu Hospital is Pune’s designated COVID-19 hospital, and its healthcare professionals are responsible for the testing, treating and well-being of quarantined patients. These brave doctors, nurses, and hospital staff, who are working round the clock, must take every precaution to remain safe from the infection. This contribution from Icertians will help provide them:

• Personal protection equipment, including N95 masks, gowns, and gloves
• Hand sanitizer dispensers
• Viral transport media (VTM) kits, which protect test samples in transit to the lab

In response to Icertis’ contribution, Shekhar Gaikwad, IAS, Commissioner of PMC; Rubal Agarwal, IAS, Additional Commissioner of PMC; and Dr Ramchandra Hankare, PMC’s health chief, stated, “On behalf of the PMC and the Naidu Hospital staff, please pass along our thanks to your Icertis colleagues – this generous contribution will help our front-line teams immensely as we unite as a community to fight this virus.”

The post Icertis Contributes Critical Personal Protection Equipment To Aid COVID-19 Healthcare Heroes appeared first on Analytics India Magazine.

The Apollo Proton Cancer Centre is a 150-bedded integrated cancer hospital that offers comprehensive cancer care. Being Southeast Asia’s first proton therapy cancer centre, Apollo has proved to be a significant milestone in efforts to battle cancer. The advanced proton therapy at Apollo is complemented by a fully integrated treatment suite that provides the most advanced treatment procedures in surgical, radiation, and medical oncology.

The Challenge

India’s healthcare industry has been one of the fastest-growing sectors, and according to a report, it is even expected to reach $280 billion by the end of this year. The country has also become one of the leading destinations for high-end diagnostic services and advanced facilities, however, the primary issue faced by Indian healthcare is the lack of awareness amongst people about the different options that are available at their disposal for the treatment of various diseases. 

APCC has put in efforts to bring in the first proton therapy centre in the country, however, the challenge was to create a trust in the minds of people about the therapy as well as the brand. Lack of awareness among people about the treatment has been the core challenge, along with the lack of trust on the best panel of doctors available. 

Usually, people were not aware that proton therapy is available in India, and would end up shelling out a large amount of money to get treated from other clinics with traditional methods. And, therefore, the objective was to generate brand awareness and brand recognition in the minds of the people about Apollo Proton Cancer Centre in general, and about the proton therapy specifically focusing on treating different types of specialities of cancer. APCC has been trying to bring in better treatment value and affordability for its patients, however, it required a robust system to make people aware about the modern technologies fighting against cancer.

The Solution

Technology has changed the landscape for the world, and newer tools like artificial intelligence and machine learning are transforming it further. The strategy APCC used was deploying ADOHM’s AI-powered tool to make people aware of the centre and proton cancer therapy. 

With the AI-powered marketing suite, ADHOM helped APCC in running campaigns that focused on creating brand affinity and also increased their number of leads. The idea was to show the right ad at the right time to the right audience. ADOHM used the power of big data analytics and incorporated machine learning techniques, along with applied propensity models and predictive analysis to deliver highly personalised communication across multiple devices and channels.

The first step of the execution was to start campaigns that are focused on creating awareness about the brand through an omnichannel approach, wherein the ads will be shown on multiple platforms using predictive bidding. For this, ADOHM’s marketing and sales platform were used to search ads focusing on how proton treatment can help people who are diagnosed with cancer and offer them comprehensive care. 

Also, with ADOHM, APCC was able to run ads on multiple platforms at once, and create campaigns in just 12-15 steps. ADOHM’s AI algorithm also helped APCC in understanding which platform or device is bringing in the most traffic or traction. The tool is also used in understanding the best time to optimise the ads in order to better target people looking for cancer treatment. 

The second step was to showcase ads that were focused on getting more leads for APCC in order to achieve the objective of conversion. The leads that had not reached the conversion stage and were lost were also retargeted to try for a ‘cancer second opinion’, wherein people could collect referral opinions from the doctors and understand the best treatment plan suited to their needs. This was done by offering different communications through dynamic creatives and continuously showing patients the remarketing ads.

Google and Facebook have been strict with certain kinds of ads in the healthcare domain, which left Apollo with limited options in terms of content play. The hospital had to put extra thrust on optimising and acing the ad delivery while keeping the cost per lead at the minimum. And, for the exact reason, APCC deployed ADOHM’s AI solution to optimise their digital campaigns at the cheapest cost. 

To explain further, Kuldeep Chaudhary, the CEO & Co-Founder, ADOHM said, that the AI algorithm deals with real-time data gathered and then builds audience profiles to serve the right ads to the right audience at the right time. The platform works on a self-learning and self-adapting module, where it tweaks the ads across channels to produce high performing campaigns 24×7. This, in turn, enables the human team to focus more on increasing the value proposition.”

ADOHM’s AI platform can understand the kind of audience checking the ads that are being run, and the data collected in the CRM further helps in optimising the ad campaigns by providing insights on the age group and the gender type. Hence, people looking for a specific type of cancer treatments were identified and targeted. 

The aim was to show people the complete journey of the treatment, right from diagnosis to post-treatment care of the individual. It was not to get individuals to come and get treated for their cancer but also allow them to have a second opinion about their treatment plan and diagnosis in order to provide better guidance. 

The Result

The move, according to the hospital, has generated positive results with a whopping 100% impact without an increase in the ad budget. While reducing the cost per lead by 50%, the AI tool helped in increasing the overall monthly leads by a sizable 60%.

“From day one the hospital had a 100% increase in their lead generation without increasing a single dime in the ad budget,” said John Chandy, the chief operating officer of Apollo Proton Cancer Centre in Chennai. “Such an improvement is indeed spectacular by any standards,” said Chandy.

With ADHOM’s dynamic creatives reaching a larger audience, the most amount of traction was seen from Adwords and Facebook in the period of two months while the campaigns were running. APCC was able to get a ROAS (Return on Ad Spend) of 4000%, considering the cost of the treatment is high. The centre saw an increase in the number of queries coming in for proton therapy by 60%. Even, remarketing campaigns helped in boosting the confidence of the brand and increased their engagements. 

Thus, educating with the right strategy at the right time and the right place has helped in increasing awareness. In short, ADOHM’s marketing and sales platform helped in running ad campaigns on an omnichannel basis, finding and engaging consumers and personalising their experience across multiple touchpoints. In turn, APCC was able to create brand awareness about their brand Apollo and about Proton Therapy to treat cancer and the different specialities of cancers that can be cured through this therapy.

ADHOM showcased the power of AI and what it can achieve in the digital marketing domain. “We, at Apollo, are very impressed with the solution, and are looking forward to seeing ADHOM in action on our future campaigns,” concluded Chandy.

The post Case Study: How This Cancer Hospital Uses AI-Powered Digital Marketing Tool To Create Brand Awareness appeared first on Analytics India Magazine.

Tricog, one of the world’s largest healthcare analytics firms, as a part of its Series B funding, has raised $10.5 million, bringing the total funding to $17.5 million; the investment comes two years after Tricog’s Series A funding. Led by prominent institutes such as The University of Tokyo Edge Capital (UTEC), Aflac Ventures, TeamFund, Dream Incubator and more, the firm is now poised to further enhance its solutions to assist in delivering superior healthcare for remote patient monitoring and to establish a strong presence in Asia and Africa, including India, Japan & China.

Founded in 2015 by Dr Charit Bhograj, Dr Zainul Charbiwala, Dr Udayan Dasgupta, and Abhinav Gujjar, Tricog leverages its deep medical and technical expertise, to provide Virtual Cardiology Services to remote clinics, powered by ground-breaking AI and medical experts. The firm offers an AI-based platform that is used by 3 million patients globally for wellness, screening and diagnosis of acute as well as chronic heart diseases.

“Tricog’s solution in low resource settings is a game-changer by allowing not only instant ECG and Echo readings without a Cardiologist present but also the ability to immediately detect life-threatening situations such as heart attacks, allowing appropriate care to be administered in a timely manner,” said Yousuf Mazhar, Partner at TeamFund.

Tricog’s Insta ECG platform has been deployed in over 2,500 Cathlabs, Hospitals, Clinics and Diagnostic Centres to help diagnose and manage patients with critical cardiac diseases, including heart attacks. The platform has been deployed across both government and private health care networks and has demonstrated a significant reduction in mortality and morbidity.

Armed with such a robust platform, Tricog quickly gained traction and expanded its offerings to 12 countries in South-East Asia and Africa. “We have witnessed phenomenal growth from our initial investments, both in terms of footprints in new geographies as well as revenue growth. Through this round of investment, we reinforce our commitment to strengthen our AI-powered platform for faster diagnosis, expand our product line and establish a strong presence in Africa and Asia including India, China and Japan,” said Dr Charit Bhograj, CEO & Founder of Tricog.

The post Tricog Raises $10.5M In Series B Funding To Further Drive Healthcare Analytics appeared first on Analytics India Magazine.

Mumbai-based healthcare AI startup Qure.ai on Thursday announced that it had raised $16 million in funding from Sequoia India and MassMutual Ventures Southeast Asia. The company uses AI to predict and analyse medical imaging to help healthcare providers in delivering exceptional care. Qure.ai offers CE certified solutions such as chest X-rays, tuberculosis screening, and head CT scans. These solutions detect critical abnormalities in a head CT scan, cranial fractures, infarcts, among others.

Equipped with robust solutions, Qure.ai has impacted over 600,000 lives by using AI on over seven million scans. The company boasts of expediting the process of interpreting scans within seconds, thereby reducing the cost of treatment while determining anomalies that can be unseen by doctors.

“The team has taken the research stage to actually impacting patient lives across more than 200 locations in 20 countries. Our products assist doctors in providing life-saving treatments, even in remote locations. This funding round will enable us to further accelerate our mission of delivering accessible and affordable healthcare for all,” says Prashant Warier, Co-founder and CEO, Qure.ai. “We have a brilliant team that’s committed to making healthcare accessible and affordable through AI. I am excited about the open research approach that Qure.ai has adopted.”

Founded in 2016, the company has quickly gained traction in the health tech landscape as penetration of AI in this domain is risky. Unlike in other sectors, a wrong prediction my AI could be fatal for patients. Thus, organisations have to ensure that they remove the flaws of AI such as bias and inaccurate predictions.

Besides, due to privacy issues in processing patients’ data, organisations limit themselves to serving the landscape. However, various companies are now getting into the market while eliminating the risk of AI. And Qure.ai is one of them that is making a difference in healthcare through their solutions.

The post Qure.ai Raises $16M To Further Enhance Its AI-Based Healthcare Solutions appeared first on Analytics India Magazine.

Innovaccer, an AI-based health tech company recently closed $70million Series C funding from Steadview Capital, Tiger Global, Dragoneer, Westbridge, Mubadala, and Microsoft’s Venture Fund M12. Founded in 2014, the India and US-based company uses analytics to assist healthcare providers in delivering superior patient-centred care. 

Over the years, Innovaccer, with its Data Activation Platform has helped its clients to unify 3.8 million patient records, resulting in a saving of more than $400million. Its platform offers population health analytics, integrated care management, AI-powered decision support and AI-powered patient engagement. The firm simplifies the data analysis process for healthcare providers, thereby allowing them to focus on their core business. To deliver this, it clubs data from different sources such as health plans, pharmacies, labs, and hospitals.

Developing data-driven healthcare is tedious due to the absence of desired data. However, the firm has mitigated problems with its platform while ensuring the privacy of users. “Despite technologies’ rise, the deep-rooted inefficiencies in healthcare make it difficult to deliver patient-centred care,” said Kanav Hasija, co-founder and chief customer success officer.

The platform provides custom insights and dashboards for enabling real-time decision making. Innovaccer application provides flexibility such that it fits the varying needs of its clients. Today, over 25,000 providers leverage its healthcare solution effortlessly to render patient-centric care. Innovaccer aims to become the go-to provider by collecting various healthcare data and enhance its platform using cutting-edge technologies.

With the new round of funds, Innovaccer is committed to strengthening its data-driven solution and help healthcare providers to streamline their workflows and make an informed decision with insights. “Everyone in healthcare struggles to identify insights and make the right decisions to deliver better care,” said Abhinav Shashank, CEO and co-founder of Innovaccer.

The post Innovaccer Raises $70M In Series C To Enhance Its Data-Driven Healthcare Platform appeared first on Analytics India Magazine.

In the Indian healthcare sector today, it has become extremely important for service providers to study and analyse the social determinants and the non-medical factors of patients, such as age, work, livelihood, socioeconomic status and education, among others, that can heavily influence patients’ health status and its outcomes. 

In a bid to stay relevant in this data-ruled world, an Iowa-based chain of hospitals, clinics, and health care facilities called MercyOne PHSO are trying to identify the prominent social determinants of health affecting their population. With more than 3,10,000 patients under 20+ value-based agreements, MercyOne PHSO’s operations are spread across seven regional delivery networks with more than 870 service locations, including 181 participant organisations and 3,700 total providers. 

The Challenge

While healthcare has a lot of data, 80% of this data is unstructured, distributed across physicians’ hand-written notes, prescriptions, and such. Putting all of this information together is necessary to create a picture of the patient, but finding it, integrating it, and analysing it is a challenge.

Additionally, success in the value-based care environment cannot be achieved based solely on clinical insights. According to a report, clinical care accounts for only 20% of health outcomes, while health behaviours, social and economic factors, and physical environments combined influence the remaining 80% of health outcomes. 

The organisation is required to streamline its strategies to enable engaged and patient-centric care across its system. To accomplish it, MercyOne PHSO set out on an aggressive data integration strategy to connect hundreds of disparate systems across its participant organisations while co-developing an integrated care management solution built on the same data activation platform.

Additionally, to ensure care delivery was holistic in every way, the organisation also needed to identify and address the prominent social determinants of health affecting their population while ensuring efficiency in care. However, the journey was obstructed by multiple challenges. Providing connectivity to tier-2 electronic health records used by rural ambulatory sites was a necessary component to deliver holistic care, but the process became costly and an inefficient venture. However, this data needed to be accessible by care teams in real-time to coordinate care across the network.

Additionally, decentralised care coordination staff and complicated workflows made task handoffs difficult across the care continuum. This also stretched and created challenges in engaging patients post-discharge. Lastly, a lot of the processes were manually-driven with paper-based surveys that had to be transcribed manually and then the data is fed into the EHR, after which it was further processed.

In order to effectively coordinate care, care teams required a daily update on admitted and discharged patients. Still, every acute facility had a different way of working lists that only captured their facility patients. Therefore, the ACO had to adopt a custom automation procedure for every practice site to absorb these feeds daily.

The Solution

In order to identify how the social determinants of health impacted population health, MercyOne PHSO developed strategies to capture non-clinical details from patients — initially deployed at three clinics and later, at four.

The healthcare provider incorporated a streamlined, AI-based data-driven approach powered by Innovaccer’s SDOH Management Solution built on top of their data activation platform to combat the clinical and non-clinical factors impeding quality of care.

Clinical data was ingested from more than 100 disparate clinical systems, including 15 different branded EHRs. Along with that the data also was standardised from multiple data sources such as payer claims, billing files, EHRs, scheduling data, public connections, and admission discharge transfer (ADT) collected from 35+ hospitals.

During this process, the patients were given surveys at registration or in the exam rooms that asked a series of questions, such as, “In the last 12 months, were you worried that your

food would run out before you got money to buy more?” When a patient completed the survey, the results were then evaluated, and based on the answers to these questions, and patients were flagged positive or negative.

In a scenario, if a patient responded ‘yes’ to requiring assistance or identified a need as urgent, a handoff occurred to a community health worker (CHW) to make sure that the patient’s social needs could be addressed. Over time, MercyOne PHSO switched to an app-based survey, compatible with iOS and supportive of multiple languages, which streamlined clinic communication with automatic email notification. This allowed community health workers to meet with patients while they were still on site and connect them with suitable community resources at the point of care.

Over time, MercyOne PHSO switched to Innovaccer’s app-based survey, compatible with iOS and supportive of multiple languages, which was given to patients at the time of registration. Leveraging the app-based survey, an automatic email notification streamlined clinic communication to allow community health workers to meet with patients, while they were still on site and connect them with suitable community resources at the point of care.

Benefits

Decentralised care coordination staff and complicated workflows made the care management tasks difficult across the continuum. Innovaccer’s suite of solutions helped MercyOne PHSO to cut down on administrative burden significantly.

By utilising the right strategies, the organisation was able to achieve the following outcomes:

MercyOne PHSO successfully conducted screenings for more than 7,000 patients across four clinics in a year, out of which 5,152 were unique, and 487 patients were identified and helped with their social needs.

Switching to the app-based survey reduced the time to document results per patient to 47 seconds, compared to 2.5 minutes with paper-based surveys. From the surveys, it was concluded that food insecurity was the top domain where patients screened positive, followed by transportation needs and health literacy.

As of now, Innovaccer is assisting MercyOne PHSO in understanding the impacts of social determinants of health on an individual patient level for around 11,000 patients. With our SDOH Management solution, MercyOne is aiming to allow the organisation to trigger care protocol based on the responses received from the patient on the survey form. Also, the next steps include the tracking of the effectiveness of the patients working with community resources for different social factors. Also, MercyOne has been trying to enhance the level of transparency and effectiveness in the communications process with the solution within the care teams.

The post Case Study: How This Chain Of Hospitals Uses AI-Powered Tools To Address Social Determinants In Healthcare appeared first on Analytics India Magazine.

Artificial intelligence is still not advanced enough to cure diseases but is very well designed to detect diseases whenever there is an outbreak of a deadly infection like the Coronavirus. Coronavirus, with over 17,205 confirmed infections as of Sunday, has already spread to countries like India. In this kind of outbreak, artificial intelligence can help in spreading awareness with various additional insights. It can also help in assisting the world health leaders by bringing in curated information about the deadly disease.

AI Supporting The Docs

The outbreak of the Coronavirus has been a time-sensitive matter as it became crucial for the researchers and the general public to know the whereabouts of the disease.

During a similar attack in China called SARS, there wasn’t enough data or information, and therefore AI had minimal applications then. But now, with the rising social media wave, the AI algorithm has plenty of information to assist the researchers. AI can take up the massive data present on social media and news sites to generate real-time information for health departments to track the widespread of the virus.

Examples Of AI Applications

Machine learning and AI operations can help world health researchers to compile and filter information about Coronavirus in order to help them make better decisions. 

Public surveillance sites like healthmap.org use AI to analyse the data from social media sites, government sites, news sites and other sources. Healthmap.org, created by Clark Freifeld and John Brownstein — an automated electronic information system for monitoring global disease outbreaks, not only does baseline surveillance using AI but also leverages the ability of AI to identify patterns. So, when the AI system diagnoses a particular group of people with Coronavirus, then AI can use the patient’s information like a zip code in order to identify the area or region from where the virus has erupted or how far the infection can travel.

Another firm Bluedot also specialises in disease surveillance, was one of the first ones to predict the outbreak of Coronavirus. They predicted the outbreak on December 31st using AI-powered system that looks through government documents, online resources, news reports, and animal and plant disease networks to warn clients against the zones pertaining to the virus. The company makes use of machine learning and NLP techniques to create models that process large amounts of data in real-time. Using this type of information, the company was able to correctly predict the spread of the virus to Seoul, Bankok, Taipei and Tokyo.

Rapid Response With AI

With the outbreak of Coronavirus, China has completely restricted travelling to Wuhan and has also informed people not to move around much in the city. While China is looking for a cure, more precaution must be taken in the correct diagnosis for this kind of disease to prevent the widespread. AI’s timely diagnosis and early detection of the disease can help the doctors in putting these patients into a separate facility for treatment to reduce the risk of spreading the virus. These AI systems can be installed in travel stations and hospital emergency wards for early detection and so that these people do not leave the affected regions of Coronavirus without receiving treatments or proper diagnosis.

Outlook

Artificial Intelligence is already making strides in the field of epidemiology for better surveillance, prediction and controlling of severe disease outbreaks like Coronavirus. With new diseases penetrating the world, more and more data is being created. And, with such massive data in the market, AI models can be trained better. In future, if AI has been augmented correctly into the system, then the possibility of even finding a cure will increase exponentially.

The post How AI Helps In Early Detection Of Coronavirus Outbreak appeared first on Analytics India Magazine.

In today’s world, where AI impacts almost every aspect of our lives, how can healthcare be left behind? Every other day, we come across a media report of a novel new approach of AI being used in healthcare and life sciences. Some of the critical areas being drug research and discovery, gene editing, disease management and treatment, pattern recognition and early diagnosis. Early detection of life-threatening diseases such as cancer, cardiovascular diseases, and neurological disorders is one of the most prominent applications of AI in healthcare.

AI is also being used for efficient hospital management by removing bottlenecks and reducing wait time for patients as well as streamlining the workflow of healthcare professionals; thereby reducing their stress. However, even though the impact of AI in healthcare is so prominent and wide-spread – it is still not impactful enough. There are multiple things holding back AI in healthcare; one of the prominent challenges being the regulations.

Regulatory Shackles

One of the key regulatory issues that are hampering the acceptance of AI in healthcare is the archaic regulatory infrastructure. Although the technological advancements in the field of healthcare have grown by leaps and bounds, the regulatory infrastructure has failed to keep up.

According to Dr Latha Poonamallee, Co-Founder & Chairperson of In-Med Prognostics, “So far, the regulations covering software as medical devices also cover AI-based software too. But AI poses different and peculiar challenges than others.”

“For example, AI-based software learns from being used more and becomes more intelligent. Most regulatory approval is based on repeatability, but when a software learns on its own, its outputs may and will vary. While that is the strength of an AI system, regulation has to change with it,” told Analytics India Magazine. 

Traditional healthcare regulations cannot be applied for AI-based treatment as it is different from a drug or a vaccine. We are dealing with machine learning, and due to its “learning” capabilities, the algorithm keeps evolving. Consider this, by the time a regulatory approval for an algorithm is granted, the algorithm “learns” from more added data and thereby evolves, and becomes a different algorithm altogether.

“Another aspect is that AI, especially neural nets, is a black box; while we can program it, we don’t really know how it works inside. That poses the problem of explicability. More than the regulatory challenge holding back AI, regulatory authorities are trying to keep pace,” added Poonamallee.

Dr Manjiri Bakre, Founder and CEO, at OncoStem Diagnostics also agrees that the “black box” nature of AI poses a unique set of regulatory issues. She said, “Though some artificial intelligence tools operate within transparency, and with easily understandable methods, others are uninterpretable, and thereby black boxes. With no explanation and understanding of how output has been reached, it makes it difficult for regulatory agencies to examine applications involving a black box AI/ML solution.”

She believes that the ability of continuous learning systems to change their output over time in response to new data is another regulatory challenge. “Black boxes as well as AI algorithms that constantly self-update, present safety concerns that have yet to be addressed by any regulatory framework,” she added.

Another area of complication when it comes to healthcare regulatory compliance and AI is managing health data and privacy. According to Dr Anurag Agrawal, “The biggest regulatory challenge is in managing data availability in a way that is lean, balanced for benefits between the data-sources and data-users, and assures privacy and prevention of misuse.” 

Dr Agrawal is a principal scientist at the CSIR Institute of Genomics & Integrative Biology (IGIB), and also a member of AITF (Artificial Intelligence Task Force) set up by the Ministry of Commerce and Industry to kick-start the use of AI for India economic transformation.

U.S. Senator Amy Klobuchar (D-MN) once said in a statement, “New technologies have made it easier for people to monitor their health, but health tracking apps, wearable technology devices like Fitbits, and home DNA testing kits have also given companies access to your private health data with very few rules of the road in place regulating how it is collected and used.”

How To Regulate AI in Healthcare

Talking about the steps that can be taken by the regulatory authorities and the AI-centric health-tech companies to broaden the horizon of the regulatory environment and smoothen the adoption of AI into healthcare, Dr Poonamallee said, “Standardisation needs to keep up with changing technological trends. US FDA is aligning with ISO standards to be globally aligned. Because in AI, the output cannot be predicted, the quality of input data may have to be more closely regulated.”

Dr Bakre also believes that the standardisation of AI/ML in healthcare applications is imperative to ensure its successful and regulated use in the future. “Not only are algorithm-based digital health tools growing exponentially, but they also require vastly different regulatory tests and analyses. As a result, regulatory agencies will need to equip employees with the expertise required to assess machine learning and other advanced technologies, while developers will need to be informed of any new or evolving regulations,” she said.

She further added, “Demonstration of conformity of AI/ML solutions with regulatory frameworks requires new standards to be developed that align with and support existing regulatory frameworks while keeping pace with evolving technologies.”

Another key issue is to treat AI in healthcare differently and more seriously. Dr Poonamallee strongly believes that healthcare cannot be unregulated like a consumer market. Unlike for e-commerce portals, where AI is just used to suggest similar products based on consumer interest and doesn’t have serious implications. The application of AI in healthcare is a serious business.

She said, “AI also has the power to put actionable data in the hands of the patient. So along with regulation for clinical decision support, there needs to be consensus on patient decision support as well.”

Talking about the steps that should be taken by health tech companies and healthcare regulatory authorities to ensure that private health data stays private and is not misused, Dr Geetha Manjunath, Cofounder and CEO of Niramai told Analytics India Magazine, “It is important for health tech companies to maintain the integrity of patient data and protect the confidentiality of health information. Patient data should also not be misused against the patient.”

“For example, if the data is used to gain insights about the patients, and then those insights are used against them in increasing insurance premium and such. One way of protecting the user from such threats is to completely anonymise the data and only allow the health tech companies to use abstract information to gain insights about the community, or use it for research and build better predictive models which will further benefit all patients,” added Manjunath.

Geetha Further said, “I think regulatory authorities should also insist on companies to document the data operating procedures in their Quality Management Systems and ensure implementation of the same through regular inspections and audits. CE mark, ISO 13485 and GDPR requirements provide such broad guidelines, which I think all health tech companies need to follow strictly.”

Dr Agrawal believes that we need to wait for the report of the Lancet-Financial Times commission on Governing Health Futures 2030 for the regulatory guidelines for the application of AI in healthcare. Governing health futures 2030: Growing up in a digital world is a joint commission formed as a result of a partnership between The Lancet and Financial Times.

According to the release, this commission will explore the convergence of digital health, artificial intelligence, and other frontier technologies with universal health coverage, focusing on the health of children and young people.

“Equitable opportunity for accessing and using health data, while respecting individual privacy, is central to providing effective and universal health coverage at a global scale. The guiding principle of such data democratisation should be – of the people, by the people, for the people,” said Dr Agrawal in a statement. He is also one of the co-chairs of this commission.

Regulating the usage of AI in healthcare is a tricky affair; wherein half-baked approaches can have serious repercussions. We need more independent, non-partisan think tanks to come together and refurbish the governance model to keep up with the digitalisation and automation of healthcare.

The post What Regulatory Challenges Holding Back The Adoption Of AI In Healthcare In 2020 appeared first on Analytics India Magazine.