Current Landscape of Curriculum Development and Implementation in Medical Artificial Intelligence: A Scoping Review

To explore the current status of medical AI educational programs and to review how these programs were developed, implemented, and improved.

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.

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.

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.