What Should the AI Era Doctor Know? A Scoping Review of Proposed Artificial Intelligence Competencies for Medical Education

Abstract

<title>Abstract</title> <underline>Background</underline> : Artificial intelligence (AI) is rapidly reshaping healthcare and the competencies expected of graduating medical students. Existing AI curricula and competency recommendations for undergraduate medical education (UME) are fragmented, and prior reviews have largely described broad themes or educational programs rather than specifying competency-level outcomes. <underline>Objectives</underline> : To systematically map and synthesize proposed AI competencies for UME. Eligibility criteria: Peer-reviewed sources proposing original AI-related competencies or learning objectives explicitly intended for undergraduate medical students. <underline>Sources of evidence</underline> : PubMed, Embase, Web of Science, and ERIC from inception to July 28, 2025, without language limits, supplemented by reference screening. <underline>Charting methods</underline> : Multiple reviewers independently screened sources and extracted verbatim competency-relevant text. Text was decomposed into discrete statements describing single AI-related skills or knowledge areas, then labelled using an agreed-upon rubric as domains, competencies, or learning objectives; statement frequencies were summarized to identify convergent areas, evidence gaps, and cross-competency relationships. <underline>Results</underline> : Of 4,071 records identified and duplicates removed, 2,877 titles/abstracts were screened and 367 full texts were assessed for eligibility. Fifty-four studies from 22 countries met inclusion criteria. From these, 564 competency-relevant statements were synthesized into a taxonomy comprising 7 domains—AI Ethics, AI Law and Regulation, AI Professionalism in Healthcare, Clinical Applications of AI, Critical Appraisal of AI Output, Research and Innovation in AI, and Theory and Foundations of AI—spanning 37 competencies and 170 learning objectives. Most sources were recent, editorial or opinion-based, and described theoretical rather than fully implemented or evaluated curricula, but showed substantial convergence on ethical/legal oversight, critical appraisal of AI outputs, and foundational understanding of AI methods and data. <underline>Conclusions</underline> : This scoping review provides a hierarchical synthesis of AI-related competencies for UME, offering a structured foundation for curriculum design and evaluation and underscoring the need for stakeholder collaboration to refine and implement a standardized and internationally actionable curriculum.

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