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Peer-reviewedCC BYMedEdPORTAL : the journal of teaching and learning resources

A Case-Based Framework for Teaching Artificial Intelligence Applications in Medical Education.

  • Rafael C Schulman1
  • Ali Shervin Razavi2
  • Céline Hardy3
  • Prithi Chakrapani4
  • Abdel Rhman Daoud5
  • Mitchell Mazza5
  • Ankita Saxena2
  • Jason Grullon6
  • Philip J Gary7
  • Frank Middleton8
  • Sriram Narsipur9
9 affiliations
  1. 1MD/PhD Student, Department of Biochemistry and Molecular Biology, College of Graduate Studies and Norton College of Medicine, SUNY Upstate Medical University.
  2. 2MD/PhD Student, Department of Neuroscience and Physiology, College of Graduate Studies and Norton College of Medicine, SUNY Upstate Medical University.
  3. 3MD/PhD Student, Department of Microbiology and Immunology, College of Graduate Studies and Norton College of Medicine, SUNY Upstate Medical University.
  4. 4Resident Physician, Department of Surgery, Northwell Health.
  5. 5MD/PhD Student, College of Graduate Studies and Norton College of Medicine, SUNY Upstate Medical University.
  6. 6Resident Physician, Department of Family Medicine, Butler Memorial Hospital - WVU Medicine.
  7. 7Assistant Professor, Department of Medicine, SUNY Upstate Medical University.
  8. 8Professor, Department of Neuroscience and Physiology, SUNY Upstate Medical University.
  9. 9Professor, Department of Medicine, SUNY Upstate Medical University.
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Abstract

Introduction

Artificial intelligence (AI) and machine learning (ML)-based tools are emerging across many areas of medicine. The next generation of medical professionals must be prepared to critically assess the tools they will use, including how they work and the ethical or legal implications of their implementation. To address this need, we developed a generalizable case-writing framework that enables educators and learners to engage with clinical cases integrating AI/ML concepts, clinical reasoning, and ethical considerations.Methods

We designed a case-writing template to compose clinical case scenarios that integrate clinical, technical, and ethical/legal learning objectives relating to AI applications in medicine. Medical students used this template to construct 3 cases. Student-authored cases were presented to a broader audience in 1-hour sessions: first as a pilot event, then as a class-wide lecture for 140 fourth-year medical students in a transition-into-residency course.Results

After the sessions, student audiences had a statistically significant increase in familiarity with key AI terminology, a greater appreciation for the importance of ethical considerations of AI applications in health care, and an increased perceived importance of integrating AI-related topics into the medical curriculum.Discussion

A case-based framework provides a scalable approach to integrating AI education into existing curricula by connecting clinical scenarios with technical concepts and the ethical considerations of AI adoption; cases offer a familiar format that helps learners understand AI applications in patient care while exploring implementation challenges. The case-writing template and 3 sample cases are provided as adaptable resources for educators seeking to incorporate AI literacy.

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Cite

BibTeXRIS
BibTeX
@article{schulman2026casebased,
  title = {{A Case-Based Framework for Teaching Artificial Intelligence Applications in Medical Education.}},
  author = {Rafael C Schulman and Ali Shervin Razavi and Céline Hardy and Prithi Chakrapani and Abdel Rhman Daoud and Mitchell Mazza and Ankita Saxena and Jason Grullon and Philip J Gary and Frank Middleton and Sriram Narsipur},
  journal = {MedEdPORTAL : the journal of teaching and learning resources},
  year = {2026},
  month = oct,
  doi = {10.15766/mep_2374-8265.11644},
  url = {https://europepmc.org/article/MED/42948325},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - A Case-Based Framework for Teaching Artificial Intelligence Applications in Medical Education.
AU  - Rafael C Schulman
AU  - Ali Shervin Razavi
AU  - Céline Hardy
AU  - Prithi Chakrapani
AU  - Abdel Rhman Daoud
AU  - Mitchell Mazza
AU  - Ankita Saxena
AU  - Jason Grullon
AU  - Philip J Gary
AU  - Frank Middleton
AU  - Sriram Narsipur
PY  - 2026
DA  - 2026/10/27
JO  - MedEdPORTAL : the journal of teaching and learning resources
DO  - 10.15766/mep_2374-8265.11644
UR  - https://europepmc.org/article/MED/42948325
AB  - Introduction  Artificial intelligence (AI) and machine learning (ML)-based tools are emerging across many areas of medicine. The next generation of medical professionals must be prepared to critically assess the tools they will use, including how they work and the ethical or legal implications of their implementation. To address this need, we developed a generalizable case-writing framework that enables educators and learners to engage with clinical cases integrating AI/ML concepts, clinical reasoning, and ethical considerations.Methods  We designed a case-writing template to compose clinical case scenarios that integrate clinical, technical, and ethical/legal learning objectives relating to AI applications in medicine. Medical students used this template to construct 3 cases. Student-authored cases were presented to a broader audience in 1-hour sessions: first as a pilot event, then as a class-wide lecture for 140 fourth-year medical students in a transition-into-residency course.Results  After the sessions, student audiences had a statistically significant increase in familiarity with key AI terminology, a greater appreciation for the importance of ethical considerations of AI applications in health care, and an increased perceived importance of integrating AI-related topics into the medical curriculum.Discussion  A case-based framework provides a scalable approach to integrating AI education into existing curricula by connecting clinical scenarios with technical concepts and the ethical considerations of AI adoption; cases offer a familiar format that helps learners understand AI applications in patient care while exploring implementation challenges. The case-writing template and 3 sample cases are provided as adaptable resources for educators seeking to incorporate AI literacy.
SN  - 2374-8265
ER  -