Abstract
BACKGROUND: Artificial intelligence (AI) is rapidly transforming healthcare, driving global demand for AI applications. Undergraduate medical education (UGME) must therefore prepare future physicians for AI‑augmented practice. However, recent reviews indicate that curriculum reform lags behind the realistic clinical use of AI, raising the question of whether we are training the right “genotype” (undergraduate) and “phenotype” (postgraduate) of physicians for the AI and digital era. This scoping review synthesizes existing evidence on AI in UGME to identify key trends and offer a practical framework for designing future AI‑integrated medical curricula. METHODS: Following the PRISMA‑ScR guidelines, we searched PubMed, Web of Science, Scopus, and gray literature for studies published between January 2019 and December 2024 on AI integration into UGME. Two reviewers independently screened titles, abstracts, and full texts, with a third resolving disagreements. Data from eligible studies were extracted and thematically analyzed using seven core themes derived from the revised Bloom’s taxonomy. RESULT: Among the 767 screened articles, 26 studies met the predefined inclusion criteria, comprising 19 review articles and 7 empirical studies. The identified integrative themes predominantly focused on foundational knowledge, AI applications, and ethical/societal implications. Although themes such as human-AI collaboration, research and innovation, cross-organizational collaboration, and evaluation/assessment were less frequently addressed, they constitute critical components for establishing a sustainable and adaptable AI curriculum framework within medical education settings. CONCLUSION: Drawing on identified practices and theoretical models, this review proposes a structured, seven-element framework to support the design and implementation of future-oriented, AI-enabled UGME curricula across medical institutions worldwide. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-026-08620-1.