Artificial intelligence (AI) Curriculum
Introduction
Artificial intelligence is becoming part of everyday medical practice, creating an urgent need to prepare future physicians to use it thoughtfully and responsibly. This curriculum grew out of Millennium Conference 2025, convened by the Carl J. Shapiro Institute for Education and Research at Harvard Medical School and Beth Israel Deaconess Medical Center and the Josiah Macy Jr. Foundation, in partnership with the Association of American Medical Colleges. Teams from eight medical schools explored how AI could support medical education while preserving independent reasoning, clinical judgment, human connection, and accountability for patient care.
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Building on that work, a broader group of educators developed five complementary modules covering AI concepts, clinical skills, limitations and biases, ethics, and professionalism. The working groups included contributors from Harvard Medical School, Morehouse School of Medicine, Northwestern University/Feinberg School of Medicine, NYU Grossman School of Medicine, Perelman School of Medicine at the University of Pennsylvania, Uniformed Services University of the Health Sciences/F. Edward Hebert School of Medicine, University of Michigan Medical School, Virginia Tech Carilion School of Medicine, University of Miami Leonard M. Miller School of Medicine, Maryland, Cincinnati, and Chicago.
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Designed for preclinical medical students, the curriculum offers materials that educators can adapt to their own settings. It is modular in nature and individual schools can incorporate the materials in ways that best suit their needs. For faculty who are less comfortable teaching these concepts, instructional notes are also provided.
Curriculum Teaching and Editing Guide​
Five-session AI curriculum • Reformatted 5 November 2026
The package uses the supplied Shapiro Institute PowerPoint template throughout. It contains five editable lecture decks, facilitator notes on every slide, and twenty supporting Word documents. This guide records the design choices and substantive content corrections made during preparation.
Session 1
Introduction to AI Concepts in Healthcare
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Led by Anita Samuel, PhD, of Uniformed Services University of the Health Sciences/F. Edward Hebert School of Medicine, this lecture introduces the major types of AI used in healthcare, how large language models generate text, and the principles of effective prompting. Students learn to interpret performance claims, understand model development and validation, and recognize why reliable use requires attention to context and
ongoing evaluation.​​
Session 2
AI in Clinical Skills & Skills Assessment
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Led by Emily Hinchcliff, MD, of Northwestern University Feinberg School of Medicine, this lecture explores AI-assisted decision support, documentation, and communication while identifying the clinical skills students must develop independently. It offers practical approaches to reasoning before consulting AI, verifying recommendations and sources, reviewing AI-generated notes, and preserving patient-centered care.
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Session 3
AI Limitations and Biases
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Led by Shirin Shafazand, MD, MS, of the University of Miami Leonard M. Miller School of Medicine, this lecture examines hallucinations, algorithmic bias, model drift, and the ways human reliance on AI can introduce error. Students learn to verify claims, evaluate whether a model applies to a particular patient or setting, and maintain independent judgment when using AI.
Session 4
Ethics of Artificial Intelligence in Medicine
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Led by Arthur Ollendorff, MD, of Virginia Tech Carilion School of Medicine, this lecture applies beneficence, nonmaleficence, autonomy, and justice to the use of AI in healthcare. Clinical scenarios help students consider appropriate use, transparency and disclosure, patients’ choices about AI, and the environmental and public health consequences of these technologies.
Session 5
AI Professionalism​
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Led by Jinjie Zheng, PhD, of Morehouse School of Medicine, this capstone lecture examines how AI use intersects with professional identity, academic integrity, and responsibility for clinical decisions. It emphasizes independent reasoning, honest disclosure, verification of AI outputs, and patient-centered practice as students develop their own standards for responsible AI use.
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