JMIR Publications

ABSTRACT

Generative artificial intelligence (AI) is rapidly entering health professions education, yet its applications in laboratory-based disciplines have not been systematically mapped. This scoping review examined how generative AI is being used in medical laboratory science, pathology, biomedical science, and molecular diagnostics education; the educational activities it supports; and the benefits, challenges, and ethical considerations reported. The review followed Joanna Briggs Institute methodology and was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. PubMed, Scopus, and the Education Resources Information Center were searched, yielding nine eligible studies published between 2024 and 2026. Applications were mapped against Laurillard’s learning types. Most studies concerned individual learner activities or educator workflow. Inquiry and acquisition were the most common learning types, while no application supported discussion or collaboration. Medical laboratory science was represented by only one study, and molecular diagnostics appeared only peripherally. Efficiency was the most frequently reported benefit, whereas factual inaccuracy and hallucination were the most common challenges. Expert oversight was widely identified as essential. Three studies independently used AI-generated errors as objects for critical evaluation, suggesting a promising educational approach. The evidence base remains small and predominantly descriptive, which is understandable in this rapidly developing field. Comparative, discipline-specific studies are needed to evaluate educational outcomes and guide responsible integration.