Choosing human over AI doctors? How comparative trust associations and knowledge relate to risk and benefit perceptions of AI in healthcare.

Abstract

The development of artificial intelligence (AI) in healthcare is accelerating rapidly. Beyond the urge for technological optimization, public perceptions and preferences regarding the application of such technologies remain poorly understood. Risk and benefit perceptions of novel technologies are key drivers for successful implementation. Therefore, it is crucial to understand the factors that condition these perceptions. In this study, we draw on the risk perception and human-AI interaction literature to examine how explicit (i.e., deliberate) and implicit (i.e., automatic) comparative trust associations with AI versus physicians, and knowledge about AI, relate to likelihood perceptions of risks and benefits of AI in healthcare and preferences for the integration of AI in healthcare. We use survey data (N=378) to specify a path model. Results reveal that the path for implicit comparative trust associations on relative preferences for AI over physicians is only significant through risk, but not through benefit perceptions. This finding is reversed for AI knowledge. Explicit comparative trust associations relate to AI preference through risk and benefit perceptions. These findings indicate that risk perceptions of AI in healthcare might be driven more strongly by affect-laden factors than benefit perceptions, which in turn might depend more on reflective cognition. Implications of our findings and directions for future research are discussed considering the conceptualization of trust as heuristic and dual-process theories of judgment and decision-making. Regarding the design and implementation of AI-based healthcare technologies, our findings suggest that a holistic integration of public viewpoints is warranted.