ArticleNPJ digital medicine2026
What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- The New Gravity: How Generative AI Remapped Innovation and What Academia Must Do Next.JMIR medical education · 2026Article
- What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education.NPJ digital medicine · 2026Article
- What and when: a Delphi study identifying AI competencies and learning objectives for medical students.Frontiers in digital health · 2026Article
- Artificial intelligence in public health education: current evidence, competencies, and a framework for responsible integration.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Artificial intelligence (AI) is rapidly reshaping healthcare and the competencies expected of graduating medical students, yet AI curricula and competency recommendations for undergraduate medical education (UME) remain fragmented. We conducted a PRISMA-ScR scoping review to map and synthesize proposed AI competencies for UME by performing a global search of PubMed, Embase, Web of Science, and ERIC without language restrictions and from database inception through July 28, 2025. Verbatim competency-relevant text was extracted and decomposed into discrete statements and classified using domains, competencies, or learning objectives. Statement frequencies were summarized to characterize recurring areas of emphasis, underrepresented topics, and cross-domain relationships. Of 4071 records identified, 54 studies from 22 countries met inclusion criteria. From 564 eligible statements, we synthesized a taxonomy comprising seven 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; theory and foundations of AI) spanning 37 competencies and 170 learning objectives. Sources were predominantly editorial/opinion, with recurring emphasis on ethicolegal oversight, critical appraisal of AI outputs, and foundational understanding of AI methods and data. This synthesis provides a structured inventory to inform curriculum planning and future stakeholder-based refinement, prioritization, and evaluation.
Identifiers
What OpenQuestion holds
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.