Healthcare AI Foundation Course (HAFC)

The Module 1 on "Foundations of Artificial Intelligence (AI) and Large Language Models (LLMs)", authored by Mr. Ravinder Singh, provides a comprehensive introduction to the core concepts of artificial intelligence and their relevance to healthcare.

The module covers the fundamentals of artificial intelligence, machine learning, neural networks, deep learning, and large language models (LLMs). It explores the AI development lifecycle, including data collection, model training, evaluation, deployment, and monitoring, while introducing learners to the major types of machine learning algorithms. It also explains how generative AI and LLMs function, highlighting both their capabilities and limitations. Through practical examples and healthcare case studies, learners develop an intuitive understanding of AI concepts and their applications in healthcare.

Finally, the module examines key challenges, including bias, uncertainty, and hallucinations, while emphasizing the importance of human oversight in the safe and responsible use of AI in healthcare.

The module serves as a foundation for the rest of the Healthcare AI Foundation Course, equipping learners with the essential knowledge needed to understand, evaluate, and responsibly use AI technologies in healthcare.

The Module 2 on "Applying AI in Everyday Healthcare Practice" co-authored by Dr Jaydeep Tank and Dr Ashlesha Tawde-Kelkar (Retd.), provides healthcare professionals with practical guidance on integrating artificial intelligence into clinical, academic, and administrative workflows.

The module introduces both general-purpose AI assistants and specialized healthcare AI tools, demonstrating how they can support tasks such as literature review, clinical decision support, scientific writing, clinical documentation, patient education, and communication. It also explores prompt engineering, the selection of appropriate AI tools, and strategies for combining multiple tools to improve productivity and workflow efficiency. Through practical demonstrations, real-world examples, and hands-on exercises, learners gain experience applying AI in routine healthcare settings.

Finally, the module emphasizes the responsible use of AI by highlighting the importance of patient privacy, verification of AI-generated information, and human oversight in clinical decision-making.

The module equips learners with the knowledge and confidence needed to use AI safely, effectively, and responsibly in everyday healthcare practice.

The Module 3 on "Clinical and Non-clinical Use Cases in Healthcare AI", co-authored by Prof. (Dr.) Kshitij Jadhav, Dr. Amit Kumar Dey and Mr. Chinmay Athaley, provides an overview of how artificial intelligence is transforming healthcare across clinical, operational, and administrative settings.

The module explores AI applications across the healthcare value chain, including medical imaging, clinical documentation, risk prediction, clinical decision support, patient engagement, and clinical coding. It also examines operational use cases such as hospital administration, capacity planning, supply chain management, and population health management. Learners are also introduced to the principles of AI development, validation, deployment, and monitoring in healthcare settings.

Finally, the module highlights the importance of bias mitigation, performance evaluation, continuous monitoring, and human oversight to ensure the safe, effective, and responsible use of AI in healthcare.

The module equips learners with a practical understanding of how AI can be integrated into healthcare systems to improve patient outcomes, enhance operational efficiency, and support evidence-based decision-making.

The Module 4 on "Making Healthcare AI Work in the Real World", co-authored by Ms. Sunita Nadhumuni and Dr. Mona Duggal, explores the factors that influence the successful adoption and implementation of artificial intelligence in healthcare.

The module examines the practical challenges of deploying AI in healthcare, including data quality, infrastructure readiness, interoperability, workflow integration, and workforce preparedness. It also highlights the importance of developing and validating AI systems using representative data and evaluating their performance in real-world settings. The module further introduces learners to the ethical, regulatory, and governance principles that support the responsible use of AI in healthcare. Key topics include transparency, accountability, privacy, clinical validation, and continuous monitoring.

Finally, the module demonstrates how technology, people, processes, and policies must work together to enable sustainable and scalable AI adoption within healthcare systems.

The module equips learners with the knowledge needed to evaluate, implement, and manage AI solutions safely, effectively, and responsibly in real-world healthcare environments.

The Module 5 on "Engaging with Patients Using AI", authored by Prof. (Dr.) Balu Krishna S, explores how artificial intelligence is transforming interactions between patients and healthcare professionals.

The module examines how patients use AI-powered tools for symptom assessment, health information, disease management, remote monitoring, and personalized care. It also introduces learners to the growing range of patient-facing technologies, including chatbots, health applications, and wearable devices. The module further explores the opportunities and challenges that arise when patients seek or act on AI-generated information. Learners are introduced to effective communication strategies, shared decision-making approaches, and methods for supporting patients in the responsible use of AI tools.

Finally, the module emphasizes the importance of patient safety, clinical judgment, human oversight, and professional accountability when incorporating AI into healthcare practice.

The module equips learners with the skills needed to engage confidently, safely, and empathetically with AI-informed patients while fostering trust and promoting high-quality care.

The Module 6 on "Interpreting AI/ML Outputs for Clinical Decision-Making", authored by Mr. Alpan Raval, equips healthcare professionals with the knowledge and skills needed to critically evaluate and interpret AI-generated insights in clinical settings.

The module introduces key performance measures used to assess healthcare AI models, including sensitivity, specificity, positive predictive value, negative predictive value, calibration, and discrimination. It also explores important concepts such as interpretability, bias, subgroup performance, and the distinction between correlation and causation. The module further examines different types of AI outputs, including predictions, classifications, measurements, recommendations, and alerts, while highlighting the importance of context when applying these outputs in clinical practice. Learners are also introduced to decision thresholds and the trade-offs involved in AI-assisted decision-making.

Finally, the module emphasizes that AI is designed to support, rather than replace, clinical judgment and demonstrates how AI-generated insights should be integrated with patient history, clinical findings, and professional expertise.

The module equips learners with the skills needed to interpret AI outputs critically and use them safely, confidently, and responsibly to support evidence-based clinical decision-making.

The Module 7 on "Healthcare AI Tools & Hands-On Experience", authored by Dr. Suvrankar Datta, provides learners with practical experience using artificial intelligence tools and technologies that are increasingly being adopted across healthcare settings.

The module introduces a range of healthcare AI applications, including AI-assisted medical imaging, natural language processing (NLP)-based clinical documentation tools, patient risk prediction systems, and generative AI applications. Learners are also introduced to healthcare datasets and gain insights into how AI models are developed and applied in clinical practice. The module further explores no-code AI tools enabling learners to understand how AI can support clinical documentation, communication, analysis, and decision-making. Through guided exercises and demonstrations, participants gain hands-on experience while exploring the strengths, limitations, and appropriate use of these technologies.

Finally, the module emphasizes the importance of responsible AI use and demonstrates how these tools can be integrated effectively into everyday healthcare workflows.

The module serves as a bridge between theory and practice, equipping learners with the skills and confidence needed to use AI tools safely, effectively, and responsibly.

Mr. Ravinder Singh
B.Tech. (Computer Science)
Global Vertical Head – Healthcare and Life Sciences at Tech Mahindra

Mr. Ravinder Singh is a healthcare technology leader with over 27 years of experience in healthcare consulting, digital transformation, and health information technology. As Global Vertical Head - Healthcare and Life Sciences at Tech Mahindra, he leads the company's global healthcare strategy, delivery, and innovation initiatives, helping healthcare providers, payers, life sciences organizations, and MedTech companies leverage AI and digital technologies to improve patient outcomes and operational efficiency.

Prior to joining Tech Mahindra, Mr. Singh held senior leadership positions at leading global technology and consulting organizations, including Senior Vice President - Consulting at CitiusTech, Senior Industry Principal - Life Sciences at Infosys, Consulting Practice Director - Health Sciences Consulting at Oracle (USA), and Business Development Manager at Cisco (USA). Throughout his career, he has led large-scale healthcare transformation programmes across the United States, Europe, the Middle East, and Asia-Pacific, with expertise spanning healthcare interoperability, health information exchange, translational medicine, population health, and enterprise digital health transformation.

Mr. Singh has advised healthcare providers, life sciences organizations, governments, and technology companies on digital transformation strategies and the adoption of AI-enabled healthcare solutions. His areas of expertise include healthcare strategy, interoperability standards, electronic health records, healthcare IT modernization, AI-driven transformation, and organizational leadership. He has also contributed to the development of digital health standards and policies in India and other developing nations and continues to be a thought leader in advancing the safe, scalable, and responsible adoption of AI in healthcare.

Dr. Jaydeep Tank
MD, DGO, DNB, FCPS, FICOG, FRCOG (Honoris Causa)
Former Past President, Federation of Obstetric and Gynaecological Societies of India (FOGSI)

Dr. Jaydeep Tank is one of India's leading obstetricians and gynaecologists with over three decades of experience in clinical practice, medical education, research, and healthcare leadership. He is the Immediate Past President of the Federation of Obstetric and Gynaecological Societies of India (FOGSI) and Chair of the Young Gynaecologists and Obstetricians Assembly (YGAA) Committee of the Asia & Oceania Federation of Obstetrics and Gynaecology (AOFOG). He has played a pivotal role in advancing women's health through national and international collaborations, quality improvement initiatives, and healthcare policy.

Dr. Tank leads several large-scale maternal and reproductive health programmes supported by the Gates Foundation, MK Hamied Foundation, and Koita Foundation, overseeing grants exceeding ₹62 crore. He has contributed to key policy initiatives, including the 2021 amendments to India's Medical Termination of Pregnancy (MTP) Act, and previously served as Chair of the Safe Abortion Committee of the International Federation of Gynecology and Obstetrics (FIGO). He has also been a strong advocate for leveraging digital health and innovation to improve women's healthcare.

A senior IVF specialist, Dr. Tank has performed over 12,000 assisted reproductive procedures since 1999. He is a Consultant at Ashwini Maternity and Surgical Hospital and Centre for Endoscopy and IVF, Partner at Tank IVF LLP and Tank Maternity and Surgical Hospital LLP, Co-Founder and Joint Managing Director of Esperanza Medical Platforms Pvt. Ltd. and Pluro IVF and Fertility, and serves as an Independent Director on the Board of API Holdings, the parent company of PharmEasy. His work continues to bridge clinical excellence, innovation, and improved access to high-quality women's healthcare.

 

Major Dr. Ashlesha Tawde-Kelkar (Retd.)
MBBS, MD, MIPHA, FRSPH
Digital Health Consultant, CHAKRA & Assistant Professor, Community Medicine, MGMIHS, Mumbai

Major Dr. Ashlesha Tawde-Kelkar (Retd.) is a public health physician, digital health expert, and medical educator with extensive experience in community medicine, healthcare management, and digital health. She currently serves as Digital Health Consultant at CHAKRA (Centre for Health, Applied Knowledge and Research Autonomy) and Assistant Professor, Department of Community Medicine, MGM Institute of Health Sciences (MGMIHS), Mumbai, where she contributes to digital health education, research, and capacity building.

A retired Major from the Indian Army Medical Corps, Dr. Tawde-Kelkar has held leadership roles in medical education and public health, training undergraduate and postgraduate students as well as healthcare professionals. She has been actively involved in developing and delivering programmes on digital health, artificial intelligence, and healthcare transformation, while mentoring research in community medicine, public health, and health administration.

Dr. Tawde-Kelkar is a Fellow of the Royal Society for Public Health (FRSPH) and has completed executive programmes from Harvard Business School and Yale School of Management. She has received several awards and recognitions for her distinguished service, including medals for her contributions in Jammu & Kashmir, and continues to be an active researcher, speaker, and advocate for the responsible adoption of digital technologies in healthcare.

Prof. (Dr) Kshitij Jadhav
MBBS, MD (Pharmacology), PhD
Assistant Professor, Koita Centre for Digital Health (KCDH), IIT Bombay

Prof. (Dr.) Kshitij Jadhav is a clinician, neuroscientist, and digital health researcher serving as Assistant Professor at the Koita Centre for Digital Health (KCDH), IIT Bombay. His work brings together medicine, artificial intelligence, data science, and engineering to develop innovative, technology-driven solutions for healthcare. He is particularly focused on translating cutting-edge AI research into practical applications that address real-world healthcare challenges.

Dr. Jadhav completed his MBBS and MD (Pharmacology) from KEM Hospital and Lokmanya Tilak Municipal General Hospital, Mumbai, followed by a PhD in Neuroscience from the University of Lausanne, Switzerland, and postdoctoral research at the University of Cambridge, UK. His research spans medical AI, machine learning, neuroscience, and digital health, with a strong emphasis on developing scalable solutions for the Indian healthcare ecosystem.

His research interests include data-efficient machine learning, federated learning, multimodal AI, and large language models for healthcare. Through his research, teaching, and interdisciplinary collaborations at IIT Bombay, Dr. Jadhav is advancing AI-driven healthcare innovation and helping build the next generation of digital health leaders.

 

Dr. Amit Kumar Dey
MBBS, MD (General Medicine), Fellow ACP (USA), FRSPH (UK), FDI, ABAIM Certified Consultant Physician
Chair, AI Technology Committee, International Diabetes Federation (IDF)

Dr. Amit Kumar Dey is a Consultant Physician specializing in diabetology, obesity, and metabolic disorders, with over 15 years of clinical experience. He currently serves as Chair of the AI Technology Committee of the International Diabetes Federation (IDF) and is certified by the American Board of Artificial Intelligence in Medicine (ABAIM). Widely recognized for his work at the intersection of diabetes care, digital health, and artificial intelligence, he is a leading advocate for the responsible adoption of AI in clinical practice.

Dr. Dey is the Founder Chair of Doctors AI, one of India's largest medical communities focused on AI in healthcare, and the founder of the Indian College of Artificial Intelligence in Medicine (ICAIM) under the Doctors AI Education and Research Foundation. He also serves as Associate Editor of JMIR Diabetes and the International Journal of Diabetes in Developing Countries (IJDDC) and is a faculty member of the National Diabetes Education Program (NDEP).

An accomplished researcher and educator, Dr. Dey has authored numerous peer-reviewed publications and book chapters and is the co-author of Decoding Artificial Intelligence for Physicians, India's first textbook on AI in Medicine. His research interests include AI in precision diabetes care, continuous glucose monitoring, and digital health innovations. He also hosts the internationally acclaimed MedAI – Decoding AI in Healthcare podcast, which reaches healthcare professionals in over 60 countries.

 

Mr. Chinmay Athaley
B.Tech, MBA
Associate Director, DeepIntent

Mr. Chinmay Athaley is a digital health leader with over 18 years of experience in healthcare technology, product management, artificial intelligence, and digital transformation across India and the United States. He currently serves as Associate Director at DeepIntent, where he works at the intersection of AI, healthcare, and digital innovation to advance technology-enabled healthcare solutions.

In addition to his industry role, Mr. Athaley is Visiting Faculty at the Indian School of Business (ISB), where he teaches AI and digital health to senior healthcare professionals and government leaders. He also serves as an advisor to the Koita Foundation, contributing to national and state-level digital health initiatives and supporting the adoption of digital health policies and capacity-building programmes across India.

Over the course of his career, Mr. Athaley has held leadership roles in software engineering, product management, programme management, and Agile transformation at organizations including Wipro, CitiusTech, Quest Diagnostics, and DaVita. He has trained leaders from the National Health Authority (NHA) and various state governments on digital health and continues to work closely with public and private sector stakeholders to drive healthcare innovation, digital transformation, and the responsible adoption of AI in healthcare.

Ms. Sunita Nadhumuni
B.E., M.S. (Electrical Engineering)
Chairperson, Arghyam

Ms. Sunita Nadhumuni is a technology leader and social entrepreneur with over two decades of experience driving digital transformation and social innovation across India and the United States. She currently serves as Chairperson of Arghyam, a philanthropic foundation focused on water security, where she leads initiatives that leverage technology, data, and digital public infrastructure to enable equitable and sustainable water management.

Prior to joining Arghyam, Ms. Nadhumuni led Dell Technologies' Global Social Innovation initiatives, where she harnessed digital technologies and strategic partnerships to improve access to healthcare, education, and livelihoods. She is also the founder of Digital LifeCare, a pioneering digital health platform developed in partnership with Tata Trusts that supports the Government of India's non-communicable disease (NCD) programme under Ayushman Bharat, enabling large-scale screening, diagnosis, and longitudinal care.

Ms. Nadhumuni has been a strong advocate for the use of digital public infrastructure and AI to address large-scale societal challenges. Her work spans digital health, public health, governance, and social impact, and she continues to advise governments, philanthropic organizations, and ecosystem partners on building scalable, technology-enabled solutions that improve public service delivery and population health.

 

Dr. Mona Duggal
MBBS, MD, MHS (Johns Hopkins Bloomberg School of Public Health)
Director, ICMR–National Institute for Research in Digital Health and Data Science (NIRDHDS)

Dr. Mona Duggal is a distinguished public health researcher and currently serves as the Director of the ICMR–National Institute for Research in Digital Health and Data Science (NIRDHDS), New Delhi. With more than two decades of experience, she has made significant contributions to public health research, epidemiology, maternal and child health, and digital health, leading initiatives aimed at strengthening India's health data ecosystem and advancing evidence-based healthcare.

Throughout her career, Dr. Duggal has led pioneering work in disease surveillance, clinical research, health informatics, and the translation of research into public health policy. Her expertise spans digital health technologies, data science, healthcare analytics, and the application of artificial intelligence to improve healthcare delivery, strengthen health systems, and support informed decision-making at the national level.

Dr. Duggal's research interests include artificial intelligence for risk stratification, mobile health (mHealth) interventions, and the use of digital technologies to improve health outcomes among vulnerable populations. In addition to her leadership role at NIRDHDS, she serves as an Adjunct Professor at the Indraprastha Institute of Information Technology (IIIT) Delhi and continues to contribute to advancing digital health innovation and research in India.

Prof. (Dr.) Balu Krishna S
MBBS, MD (Internal Medicine)
Senior Faculty, Christian Medical College (CMC), Vellore

Prof. (Dr.) Balu Krishna S is a senior clinician, academician, and physician at the Christian Medical College (CMC), Vellore, with extensive experience in internal medicine, medical education, clinical research, and digital health. His work focuses on delivering evidence-based, patient-centred care while leveraging digital technologies to improve clinical outcomes and healthcare delivery.

Throughout his career, Prof. (Dr.) Balu Krishna has played a pivotal role in undergraduate and postgraduate medical education, mentoring clinicians, researchers, and healthcare professionals. He has been actively involved in the adoption and implementation of electronic medical records, clinical information systems, and data-driven approaches to healthcare, with a particular emphasis on improving continuity of care and generating real-world clinical evidence.

Prof. (Dr.) Balu Krishna has authored numerous peer-reviewed publications and has contributed significantly to advancing the use of longitudinal patient data in clinical practice and research. Through his work in patient care, education, and digital health, he continues to promote the responsible adoption of technology-enabled healthcare solutions and strengthen data-driven decision-making within the healthcare ecosystem.

Mr. Alpan Raval
MS (Physics), PhD (Theoretical Physics)
Chief Scientist, AI/ML, Wadhwani AI

Mr. Alpan Raval is the Chief Scientist, AI/ML at Wadhwani AI, an independent non-profit institute focused on developing artificial intelligence solutions to address large-scale societal challenges in healthcare, agriculture, and education. With more than two decades of experience across academia and industry, he has built extensive expertise in machine learning, computational biology, and the application of AI to solve complex real-world problems.

Before joining Wadhwani AI, Mr. Raval held research leadership positions at LinkedIn, Amazon, and D. E. Shaw Research, where he led teams working on applied machine learning, computational science, and large-scale data analytics. His research interests include machine learning, computational biology, biological networks, statistical mechanics, and the development of AI systems that can deliver measurable social impact.

Mr. Raval earned his Master of Science in Physics from the Indian Institute of Technology (IIT) Kanpur and his PhD in Theoretical Physics from the University of Maryland. He subsequently completed postdoctoral research at the University of Wisconsin and served as a professor at the Keck Graduate Institute (KGI) and Claremont Graduate University (CGU), where he directed the joint doctoral programme in Computational and Systems Biology. Through his work at Wadhwani AI, he continues to advance the responsible development and deployment of AI-driven solutions in healthcare and beyond.

Dr. Suvrankar Datta
MBBS, MD (Radiodiagnosis), AIIMS New Delhi
Faculty Fellow, Koita Centre for Digital Health - Ashoka (KCDH-A)

Dr. Suvrankar Datta is a radiologist, researcher, and digital health innovator with expertise in medical imaging, artificial intelligence, and healthcare technology. As a Faculty Fellow at the Koita Centre for Digital Health – Ashoka (KCDH-A), he works at the intersection of clinical medicine, data science, and AI to develop solutions that enhance diagnostic accuracy, improve clinical workflows, and strengthen healthcare delivery.

A gold medallist from JIPMER, Puducherry, Dr. Datta completed his MD in Radiodiagnosis from AIIMS, New Delhi. His research focuses on the application of artificial intelligence in radiology, including automated image analysis, clinical decision support, and the use of large language models (LLMs) in radiology reporting. His work has received national and international recognition, including the prestigious Radiological Society of North America (RSNA) Trainee Research Prize.

Beyond his research activities, Dr. Datta is actively involved in medical leadership and advocacy and has served as President of the Federation of All India Medical Association (FAIMA). Through his interdisciplinary work spanning medicine, technology, and policy, he continues to advance the responsible adoption of artificial intelligence in healthcare while contributing to the development of the next generation of digital health professionals.