ArticleBMC medical education2026
Framework for AI integration into the undergraduate medical curricula: a scoping review.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education.NPJ digital medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial 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.
methodsFollowing 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.
resultAmong 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.
conclusionDrawing 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.