Barriers to and facilitators of clinician acceptance and use of artificial intelligence in healthcare settings: a scoping review

Abstract

This scoping review highlights key gaps in understanding clinician acceptance and use of AI in healthcare, including the limited examination of individual moderators and context-specific factors in LMICs. While universal determinants such as performance expectancy and facilitating conditions were consistently identified across settings, factors not covered by the UTAUT framework such as clinician hesitancy, relational dynamics, legal and ethical considerations, technical features and clinician involvement emerged with varying impact depending on the level of healthcare context. These findings underscore the need to refine frameworks like UTAUT to incorporate context-specific drivers of AI acceptance and use. Future research should address these gaps by investigating both universal and context-specific barriers and expanding existing frameworks to better reflect the complexities of AI adoption in diverse healthcare settings.

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