Responsible AI for Health Care: Concepts and Applications

• 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

• Led by Harvard faculty with deep expertise in AI, ethics, regulation, and healthcare implementation 
• Explore real-world case studies of both successful and flawed AI implementations 
• Engage with ethical and regulatory frameworks shaping AI in healthcare globally 
• Gain tools to evaluate and monitor AI systems, including bias detection, transparency practices, and risk assessments 
• Participate in interactive learning through group discussion, applied exercises, and optional Q&A 
• Develop strategies for responsible AI implementation aligned with clinical, regulatory, and organizational realities 
• Join a global network of healthcare professionals committed to safe, equitable, and effective AI 

Tackling AI Challenges in Healthcare: 

• Diagnosis
  Responsible AI leverages large-scale multimodal health data to enhance diagnostic accuracy, reduce misdiagnosis, and ease clinician workload, all while prioritizing ethical data use and patient privacy.
• Precision Medicine
  By enabling data-driven, patient-specific treatment plans, responsible AI shifts care from generic protocols to personalized medicine. This approach improves outcomes, optimizes resource use, and ensures equity through bias-aware, ethical design.
• Prediction Models
  Ethical AI-powered prediction models support clinicians in forecasting disease progression and tailoring interventions. These models enhance decision-making, reduce risks, and promote transparent, accountable care strategies.
• Course Focus
  To unlock AI’s full potential and prevent harm, practitioners must understand the responsibilities that extend beyond algorithm development. This course emphasizes pre- and post-deployment considerations, including algorithmic bias, equity, and public health impacts. Students will gain the tools to implement responsible AI solutions that improve patient outcomes and drive systemic efficiency.

Throughout this program, you’ll learn directly from distinguished Harvard faculty and leading industry experts. Interactive live sessions and small group discussions provide plenty of opportunities to ask questions and exchange experiences with faculty. Core instructors include:

• Trishan Panch, MD, MPH: Instructor, Harvard T.H. Chan School of Public Health
• Heather Mattie, PhD, SM, MS: Lecturer, Co-Director, Health Data Science Master’s Program, Director of EDIB Programs, Harvard T.H. Chan School of Public Health
• John Brownstein: Chief Innovation Officer, Boston Children’s Hospital. Professor of Pediatrics, Harvard Medical School
• Ray Campbell: President, FAIR Health
• Lindsay Jubelt: Chief Clinical Officer, Optum
• Emily Lindemer, PhD: VP Data & Healthcare Innovation, Morgan Health – JP Morgan Chase
• Gianluca Mauro: CEO, AI Academy
• Santiago Romero-Brufau, MD, PhD: Director of AI and Systems Engineering, Mayo Clinic. Adjunct Assistant Professor, Harvard T.H. Chan School of Public Health