A Scoping Review of Vibe Coding in Healthcare Across Clinical, Administrative, and Research Domains
Abstract
Abstract Importance : Large language model (LLM)–assisted code generation, termed “vibe coding,” has the potential to reshape how healthcare tools are built and deployed, enabling clinicians, researchers, and administrators to directly translate domain expertise into functional software. Objective : To map the current landscape of vibe coding and adjacent LLM-assisted code generation as applied across healthcare domains, including clinical care delivery, medical education, healthcare infrastructure, administrative operations, and biomedical research. Evidence Review : Following the Arksey and O’Malley framework and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, we searched PubMed, Scopus, Web of Science, IEEE Xplore, and the ACM Digital Library using terms encompassing vibe coding and LLM-assisted software development intersected with healthcare terminology. Peer-reviewed literature published from January 2023 through December 2025 was included. Grey literature was referenced selectively for qualitative context. Findings : The peer-reviewed literature on vibe coding in healthcare is early, with the first publications appearing in mid-2025. Key domains include clinician-built interactive training tools for medical education, AI-assisted data analysis pipelines and clinical decision support prototypes in biomedical research, healthcare interoperability infrastructure, and administrative workflow…
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