Authors
Michel S Kabbash, Tyler J Loftus, Jason Morrow, Daniel Dent, Ronald Rodriguez
Published in
Advances in medical education and practice. Volume 17. Pages 640400. Epub Oct 07, 2026.
Abstract
Although Artificial Intelligence (AI) is increasingly embedded in clinical practice and Graduate Medical Education (GME), AI-specific competencies are not yet explicitly represented as a distinct domain within the Accreditation Council for Graduate Medical Education's (ACGME) six core competency framework. The ACGME's ongoing consideration of AI in its Common Program Requirements revision process signals an emerging recognition of the need to address this gap. Over 1200 FDA-authorized AI medical devices, unregulated generative AI tools in clinical workflows, and a proliferating state legislative landscape create urgent educational needs. We conducted a purposive narrative review of literature addressing AI adoption, documented failures and litigation, regulatory developments, and bioethical frameworks to develop AI-specific learning objectives, milestone language, and assessment strategies mapped to each ACGME competency. We propose the SHARED ethical framework - Stewardship, Humanity, Accountability, Responsiveness, Equity, and Disclosure - as the moral foundation for AI education. We map AI-related capabilities and representative developmental descriptors across the six existing ACGME competency domains and propose corresponding curricular content and assessment approaches. The alternative to systematic, integrated AI training - informal adoption without standards or assessment - poses unacceptable risks to patients, trainees, and the profession. The proposed framework provides a structured foundation for further development and evaluation of AI education within competency-based GME.
PMID:
42861229
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.
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