Responsible AI for Health Care: Concepts and Applications
“I went in with close to zero knowledge regarding AI in health care and finished the course with a plethora of knowledge I hope to apply in my realm of practice. The future is here.”
—Deep Palikhel, Physician Assistant at Baylor Scott & White Hospital
About the Program
This program aims to unveil the core principles of responsible AI, the capabilities of Large Language Models and Generative AI, and their profound implications for healthcare, emphasizing ethical considerations and safety measures.
Program Fees
- Standard Price $2,600.00
Program Overview
AI is Transforming Industries—Understand its Role in Healthcare’s Future
Large Language Models (LLMs), machine learning, and other forms of artificial intelligence (AI) are reshaping how healthcare is delivered, managed, and regulated. But with this promise comes complexity and risk. Responsible AI for Health Care: Concepts and Applications is a four-day, intensive live online program that equips healthcare professionals with the knowledge and tools to critically assess, implement, and govern AI technologies in clinical, operational, and public health settings.
Led by renowned Harvard faculty and AI ethics experts, this program focuses on ethical principles, regulatory frameworks, fairness, transparency, and implementation science. Participants will examine real-world case studies—both successes and failures—to explore how to apply responsible AI in high-stakes environments while avoiding unintended harms.
Through interactive lectures, group discussion, and applied learning, participants will engage with timely questions at the intersection of innovation, safety, and health equity. The program also fosters a strong peer network, designed to support continued learning and collaboration well beyond the course.
Upcoming Program Details
- Define foundational AI concepts and differentiate types of AI tools relevant to clinical, operational, and population health use cases
- Identify sources of algorithmic bias stemming from data, design, or deployment, and evaluate their impact on health equity
- Apply leading ethical frameworks to assess fairness, transparency, accountability, and safety in healthcare AI
- Compare and contrast U.S. and global regulatory approaches to healthcare AI, and assess implications for compliance and innovation
- Evaluate the trade-offs between explainability and model complexity, and explain the role of transparency in building trust in clinical AI
- Assess post-deployment risks and accountability mechanisms, including model drift, adverse event reporting, and human oversight
- Analyze real-world AI successes and failures to identify lessons learned and implementation challenges
- Develop strategies for responsible AI implementation aligned with ethical standards, clinical workflows, and organizational readiness
All Times are Eastern Time (ET).
| Monday, February 2, 2026 | ||
|---|---|---|
| 9:00–10:00 am | Introduction to AI I | |
| 10:00–10:15 am | Break | |
| 10:15–11:15 am | Introduction to AI II | |
| 11:15–11:30 am | Break | |
| 11:30 am–12:30 pm | Interactive Session | |
| 12:30–1:00 pm | Office Hours (Q&A) | Tuesday, February 3, 2026 |
| 9:00–10:00 am | Implementing AI in Healthcare Organizations | |
| 10:00–10:15 am | Break | |
| 10:15–11:15 am | Algorithmic Bias and Data Ethics | |
| 11:15–11:30 am | Break | |
| 11:30 am–12:30 pm | Interactive Session | |
| 12:30–1:00 pm | Office Hours (Q&A) | Monday, February 9, 2026 |
| 9:00–10:00 am | Safety and Regulation I | |
| 10:00–10:15 am | Break | |
| 10:15–11:15 am | Safety and Regulation II | |
| 11:15–11:30 am | Break | |
| 11:30 am–12:30 pm | Interactive Session | |
| 12:30–1:00 pm | Office Hours (Q&A) | Tuesday, February 10, 2026 |
| 9:00–10:00 am | Evaluating and Scaling AI in Healthcare | |
| 10:00–10:15 am | Break | |
| 10:15–11:15 am | AI and Intrapreneurship | |
| 11:15–11:30 am | Break | |
| 11:30 am–12:30 pm | Interactive Session and Closing | |
| 12:30–1:00 pm | Office Hours (Q&A) |
This agenda is subject to change.
Current faculty, subject to change
John Brownstein
Professor of Pediatrics
Harvard Medical School
Ray Campbell
President
FAIR Health
Lindsay Jubelt
Chief Clinical Officer
Optum
Emily Lindemer
Head of GTM & Operations
Protege
Heather Mattie
Lecturer on Biostatistics, Co-Director, Health Data Science Master’s Program, Director of EDIB Programs
Department of Biostatistics
Harvard T.H. Chan School of Public Health
Gianluca Mauro
CEO
AI Academy
Trishan Panch
Instructor
Harvard T.H. Chan School of Public Health
Santiago Romero-Brufau
Director of AI and Systems Engineering
Department of Otolaryngology — Head and Neck Surgery
Mayo Clinic
Adjunct Assistant Professor
Department of Biostatistics
Harvard T.H. Chan School of Public Health
This online program is designed for senior managers and executives who are responsible for developing and implementing AI strategy in their organizations and are looking to understand AI, its current state of the art, and future.
Participants will come from a range of organizational functions including health care delivery, health care technology, primary care systems, payers, and governments. Some titles represented in the program will include:
- Chief Executive Officer
- Chief Information Officer
- Chief Innovation Officer
- Chief Medical Informatics Officer
- Chief Medical Officer
- Clinician
- Data Scientist
- Director
- Engineer
- Innovation Specialist
- Finance Professional
- Product Manager
- Project Manager
- Venture Capital Investor
Credits & Logistics
Harvard T.H. Chan School of Public Health will grant 1.2 Continuing Education Units (CEUs) for this program, equivalent to 12 contact hours of education. Participants can apply these contact hours toward other professional education accrediting organizations.
All credits subject to final agenda.
All participants will receive a Certificate of Participation upon completion of the program.
This program also contributes to the AI in Health Care Certificate of Specialization. Click here for more information.
Advance Your Career at Harvard with Responsible AI for Health Care: Concepts and Applications