An audit of AI-related documents across U.S. medical schools

A framework-based qualitative content analysis

Dados Bibliográficos

ID21449031
AutoresEmily Rush (0009-0008-1656-2207, Rush University, autor correspondente), Jessica N Byram (0000-0001-7097-8352, Indiana University School of Medicine), Colleen N Garnett (0000-0002-9426-9304, Department of Cell, Developmental, & Integrative Biology, University of Alabama at Birmingham Heersink School of Medicine), Nicole DeVaul (0000-0003-2623-5003, Department of Anatomy and Cell Biology, The George Washington University School of Medicine and Health Sciences), Laura Smith (0000-0001-5151-8641, Rush University), Laura J Smith (0000-0003-3275-1530, Rush University), Margaret Checchi (Rush University), Daniel Martin (0000-0001-6483-3923, Indiana University School of Medicine), Leslie A Hoffman (0000-0002-2251-0648, Indiana University School of Medicine), Kirsten Brown (0000-0001-9425-7672, Department of Anatomy and Cell Biology, The George Washington University School of Medicine and Health Sciences), Daniel J Mumbower (Indiana University School of Medicine), Robert M Becker (Indiana University School of Medicine), Victoria A Roach (0000-0003-4020-2224, University of Washington), Alison F Doubleday (Department of Oral Medicine and Diagnostic Sciences and Director of Faculty Development, University of Illinois Chicago College of Dentistry), Danielle N Edwards (0000-0001-5087-0029, Department of Anatomical Sciences and Neurobiology, University of Louisville School of Medicine), Rebecca S Lufler (0000-0002-6495-5071, Department of Medical Education, Tufts University School of Medicine), Alexandra Wactor (0000-0002-8735-7007, Department of Medical Education, Tufts University School of Medicine), Sophia Boxerman (Rutgers New Jersey Medical School), Suzanne Smith (0000-0002-8486-7087, Department of Medical Education, Tufts University School of Medicine), Hannah Herriott (Regis University), Adam B Wilson (0000-0002-1221-5602, Rush University)
Ano2026
Volume48
Fascículo3
Páginas493-505
Data de publicação2026-03-04
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoMedical Teacher (JOURNAL)
Identificadores do periódicoISSN: 0142-159X • E-ISSN: 1466-187X
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/0142159x.2025.2564869
PMID41021415
OpenAlexW4414687484
IdiomaEN
Citações recebidas1
Referências citadas25

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

Audit · Balance (ability) · Content analysis · MEDLINE · Qualitative analysis · Qualitative research · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI