ReviewJournal of multidisciplinary healthcare2026
Current Landscape of Curriculum Development and Implementation in Medical Artificial Intelligence: A Scoping Review.
Review in Journal of multidisciplinary healthcare, 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To explore the current status of medical AI educational programs and to review how these programs were developed, implemented, and improved. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, this study searched English articles from 2021 to April 2026 published in PubMed Central, Web of Science, Scopus, and MEDLINE databases. All curricula included in this review were implemented programs, not theoretical frameworks. Results: Among the 7652 screened documents, 36 were identified. Data extraction focused on programs' status, construction methods, and course frameworks. The number of programs has increased rapidly but remains small, with most in the pilot stage. Learners were predominantly medical students, with small class sizes. Curriculum development mainly relied on expert experience. Learning objectives mainly focused on the understanding level, but also covered advanced cognitive skills, and the content, covering AI principles, clinical applications, and ethics, demanded high cognitive engagement. Most programs were offered as modular elective courses in online or face-to-face formats, employing diverse teaching methods and activities. The learning outcomes and course evaluations mainly relied on student feedback. This review identified persistent gaps: the lack of standardized curriculum frameworks, affective learning objectives, and objective assessment tools; limited content on AI development, applications, collaboration, communication; weak integration with existing curricula; and insufficient involvement of learning designers. Conclusion: Medical AI education programs are still in the initial stage. There is a need to accelerate curriculum development pathways, enhance practical skill trainings and affective competency developments, and establish effective instructional design and evaluation systems.
Indexed as
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.