Framework for artificial intelligence implementation research in healthcare: synthesizing current evidence on barriers and facilitators

Existing implementation frameworks like CFIR, UTAUT, and NASSS fail to capture AI-specific challenges — data quality and portability, model drift, algorithmic bias, medical-legal ambiguity, and eroded trust over extended use — so this scoping review of 142 articles consolidated the evidence into the Framework for Artificial Intelligence Implementation Research in Healthcare (FAIIR-H), spanning 63 constructs across five themes: Design and Development, Organization and Culture, Deployment and Maintenance, with Quality and Safety and Equity as overarching themes.