ArticleResearch square2026
Artificial Intelligence Integration in U.S. Healthcare Professional Degree Programs: A Rapid Scoping Review.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Purpose: Artificial intelligence (AI) capabilities and use in health care have expanded rapidly, creating an urgent need to adapt health professions curricula. This study analyzed peer-reviewed evidence (2019-2026) on AI integration in health professional degree education relevant to U.S. programs, characterized study designs and findings, identified research gaps, and provided future directions for scalable, ethically governed AI curricula. Method: A rapid scoping review approach was applied using PubMed and Google Scholar as primary sources, supplemented by citation chaining of high-yield syntheses and targeted retrieval of authoritative U.S. curricular landscape resources and selected institutional exemplars (not counted as peer-reviewed studies). Inclusion targeted English-language, peer-reviewed studies (2019-2026) addressing artificial intelligence/machine learning/generative artificial intelligence education in health professions education, including surveys, curriculum evaluations, qualitative studies, consensus/Delphi, and scoping/systematic reviews. Data were extracted into evidence-mapping Tables (study characteristics and methods summary) and descriptively synthesized by profession, content domains, and evaluation outcomes. Results: Thirty-six peer-reviewed papers meeting the inclusion criteria were synthesized. Study designs were skewed toward reviews (integrative/scoping/systematic) and surveys; fewer evaluated educational interventions with objective pre/post outcomes. Medical education, especially radiology, accounted for many implementations and studies, while dentistry, pharmacy, and physical therapy education had smaller but growing bodies of work. Evidence syntheses consistently call for competency-based curricular design, faculty development, assessment redesign for generative AI, and governance aligned with ethical and risk-management frameworks. Conclusions: U.S. health professions programs are moving from ad hoc AI exposure to structured offerings emerging from national organizations and institutional exemplars. However, the literature base remains early, with limited multi-institutional evaluations, minimal longitudinal follow-up, and weak linkage to clinical performance or patient outcomes. Next-generation research should prioritize competency-linked curricula, transparent evaluation methods, and equity- and safety-centered governance.
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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.