Understanding Clinician’s Risk and Benefit Perceptions of Artificial Intelligence

ABSTRACT: This study examines clinicians’ perceptions of the risks and benefits associated with artificial intelligence (AI) in healthcare, exploring how these perceptions influence their intentions to adopt AI tools. Using the Value-based Adoption Model (VAM), the research evaluates factors such as performance anxiety, liability concerns, social biases, and trust in AI. A cross-sectional survey of 175 American clinicians was conducted, and the data were analyzed using partial least squares (PLS) structural equation modeling. Results indicate that perceived performance anxiety, liability issues, and social biases significantly contribute to clinicians’ perceived risks of AI, while perceived trust in AI positively influences perceived benefits but not perceived risks. Both perceived risks and benefits were found to influence clinicians’ intentions to adopt AI technologies, with benefits playing a stronger role. The study highlights the importance of addressing clinicians’ concerns related to AI performance, liability, and social biases to foster trust and enhance AI adoption in healthcare settings. The findings have implications for policymakers, healthcare organizations, and AI developers, suggesting that improving the explainability and reliability of AI tools and addressing ethical concerns are crucial for their successful integration into clinical practice. The research offers insights for future work on AI adoption in healthcare.