An audit of AI-related documents across U.S. Medical schools: A framework-based qualitative content analysis

Authors

• Emily Rush, Office of Academic Affairs and Department of Anatomy and Cell Biology, Rush University, Chicago, IL, USA.
• Jessica N. Byram, Department of Anatomy, Cell Biology & Physiology, Indiana University School of Medicine, Indianapolis, IN, USA.
• Colleen N. Garnett, Department of Cell, Developmental, & Integrative Biology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA.
• Nicole DeVaul, Department of Anatomy and Cell Biology, The George Washington University School of Medicine and Health Sciences, Washington, D.C., USA.
• Laura Smith, Center for Teaching Excellence and Innovation (CTEI), Rush University, Chicago, IL, USA.
• Margaret Checchi, Center for Teaching Excellence and Innovation, Rush University, Chicago, IL, USA.
• Daniel Martin, Department of Anatomy, Cell Biology & Physiology, Indiana University School of Medicine, Fort Wayne, IN, USA.
• Leslie A. Hoffman, Department of Anatomy, Cell Biology & Physiology, Indiana University School of Medicine, Fort Wayne, IN, USA.
• Kirstin M. Brown, Department of Anatomy and Cell Biology, The George Washington University School of Medicine and Health Sciences, Washington, D.C., USA.
• Daniel J. Mumbower, Department of Anatomy, Cell Biology & Physiology, Indiana University School of Medicine, Indianapolis, IN, USA.
• Robert M. Becker, Department of Anatomy, Cell Biology & Physiology, Indiana University School of Medicine, Indianapolis, IN, USA.
• Victoria A. Roach, Department of Surgery, University of Washington, Seattle, Washington, USA.
• Alison F. Doubleday, Department of Oral Medicine and Diagnostic Sciences and Director of Faculty Development, University of Illinois Chicago College of Dentistry, Chicago, IL, USA.
• Danielle N. Edwards, Department of Anatomical Sciences and Neurobiology, University of Louisville School of Medicine, Louisville, KY, USA.
• Rebecca S. Lufler, Department of Medical Education, Tufts University School of Medicine, Boston, MA, USA.
• Alexandra Wactor, Department of Medical Education, Tufts University School of Medicine, Boston, MA, USA.
• Sophia Boxerman, Department of Obstetrics and Gynecology, Rutgers New Jersey Medical School, Newark, NJ, USA.
• Suzanne Smith, Department of Medical Education, Tufts University School of Medicine, Boston, MA, USA.
• Hannah Herriott, School of Rehabilitative & Health Sciences, Rueckert-Hartman College of Health Professions, Regis University, Denver, CO, USA.
• Adam B. Wilson, Department of Anatomy and Cell Biology, Rush University, Chicago, IL, USA.

Document Type

Journal Article

Publication Date

9-29-2025

Journal

Medical teacher

DOI

10.1080/0142159X.2025.2564869

Keywords

Medical education; Qualitative content analysis; United States; artificial intelligence; educational policy

Abstract

PURPOSE: Medical schools would benefit from systematic guidance for developing comprehensive artificial intelligence (AI) policies, given generative AI's rapid integration into medical education. This study developed and applied an idealized AI policy framework to analyze AI-related documents at U.S. medical school institutions, providing reference points for the development and refinement of institutional policies. METHODS: AI-related documents from institutions with U.S. allopathic and osteopathic medical schools were systematically collected (from August to October 2024) and analyzed using a comprehensive framework containing 24 subthemes across six themes: Background/Context, Governance, AI Literacy, Tools/Usage, Ethical/Legal Considerations, and Technology Support and Infrastructure. Publicly available online documents were systematically coded to generate framework subtheme scores indicating breadth of coverage across framework themes. RESULTS: AI-related documents retrieved from 73.7% (146/198) of U.S. medical school institutions covered an average of 8 of 24 subthemes, representing a mean framework coverage score of 32.3% ± 19.8 Rarely addressed subthemes included Audit and Compliance Mechanisms (6.8%, 10/146), Technical Infrastructure (6.2%, 9/146), and Environmental Stewardship (1.4%, 2/146). Academic Honesty and Plagiarism dominated AI-related documents (81.5%, 119/146), followed by Decision-Making Authority (54.1%, 79/146) and Critical Evaluation (52.1%, 76/146). Formal AI policies demonstrated significantly higher framework coverage than other AI document types (44.0% vs 30.4%, p = 0.003). Seven institutions with the highest coverage (≥13/24 subthemes) shared seven common distinguishing features, with six present universally. CONCLUSIONS: AI-related documents currently emphasize academic integrity over strategic planning, with substantial gaps in infrastructure and review mechanisms. Institutions can enhance their AI policies by incorporating common features identified in well-designed policies and following frameworks that strike a balance between immediate concerns and long-term adaptability.

APA Citation

Rush, Emily; Byram, Jessica N.; Garnett, Colleen N.; DeVaul, Nicole; Smith, Laura; Checchi, Margaret; Martin, Daniel; Hoffman, Leslie A.; Brown, Kirstin M.; Mumbower, Daniel J.; Becker, Robert M.; Roach, Victoria A.; Doubleday, Alison F.; Edwards, Danielle N.; Lufler, Rebecca S.; Wactor, Alexandra; Boxerman, Sophia; Smith, Suzanne; Herriott, Hannah; and Wilson, Adam B., "An audit of AI-related documents across U.S. Medical schools: A framework-based qualitative content analysis" (2025). GW Authored Works. Paper 7889.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/7889

Department

Anatomy and Regenerative Biology