OBJECTIVES: This study aimed to systematically map the evidence and identify patterns of barriers and facilitators to clinician artificial intelligence (AI) acceptance and use across the types of AI healthcare application and levels of income of geographic distribution of clinician practice. DESIGN: This scoping review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guideline. DATA SOURCES: PubMed and Embase were searched from 2010 to 21 August 2023. ELIGIBILITY CRITERIA: This scoping review included both empirical and conceptual studies published in peer-reviewed journals that focused on barriers to and facilitators of clinician acceptance and use of AI in healthcare facilities. Studies that involved either hypothetical or real-life applications of AI in healthcare settings were included. Studies not written in English and focused on digital devices or robots not supported by an AI system were excluded. DATA EXTRACTION AND SYNTHESIS: Three independent investigators conducted data extraction using a pre-tested tool meticulously designed based on eligibility criteria and constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to systematically summarise data. Subsequently, two independent investigators applied the framework analysis method to identify additional barriers to and facilitators of clinician acceptance and use in healthcare settings, extending beyond those captured by UTAUT. RESULTS: The search identified 328 unique articles, of which 46 met the eligibility criteria, including 44 empirical studies and 2 conceptual studies. Among these, 32 studies (69.6%) were conducted in high-income countries and 9 studies (19.6%) in low-income and middle-income countries (LMICs). In terms of the types of healthcare settings, 21 studies examined primary care, 26 focused on secondary care and 21 reported on tertiary care. Overall, drivers of clinician AI acceptance and use were ambivalent, functioning as either barriers or facilitators depending on context. Performance expectancy and facilitating conditions emerged as the most frequent and consistent drivers across healthcare contexts. Notably, there were significant gaps in evidence examining the moderator effect of clinician demographics on the relationship between drivers and AI acceptance and use. Key themes not encompassed by the UTAUT framework included physician involvement as a facilitator and clinician hesitancy and legal and ethical considerations as barriers. Other factors, such as conclusiveness, relational dynamics, and technical features, were identified as ambivalent drivers. While clinicians' perceptions and experiences of these drivers varied across primary, secondary and tertiary care, there was a notable lack of evidence exclusively examining drivers of clinician AI acceptance in LMIC clinical practice. CONCLUSIONS: 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.