Abstract

Medicine is defined by uncertainty. Physicians routinely face clinical scenarios where their immediate knowledge falls short, and they turn to colleagues, databases, or literature to fill the gap. Generative AI (GenAI) introduces a novel advisory source to this workflow, combining the scale of a medical database with the conversational interface of a peer consultation. Because GenAI bridges static data retrieval and dynamic interaction, it has the potential to alter established cognitive routines in clinical decision-making. To understand the implications of this shift, this study addresses two research questions: (1) When facing uncertainty, how do physicians seek information and weight different sources? (2) How does GenAI consultation impact diagnostic decisions? We ground the investigation into two complementary theoretical lenses. Dual-Process Theory (Kahneman, 2011) captures the cognitive mechanism; GenAI delivers what looks like slow, analytical reasoning at the speed of fast, intuitive thought, potentially creating a shortcut that discourages physicians from engaging in their own critical analysis. The Judge-Advisor System (Sniezek & Buckley, 1995) captures the behavioral process; how physicians, as decision-makers, weigh and integrate advice from external sources before arriving at a final judgment. Together, these frameworks assess both the internal cognitive shifts and the practical behavioral changes occurring when GenAI is introduced into clinical workflows. This study will adopt a randomized controlled experiment using clinical case vignettes. Physicians varying in specialty and expertise will provide an initial diagnosis and confidence rating before being randomly assigned to consult GenAI, a conventional medical database, or a human colleague. Participants will then submit revised diagnoses and confidence scores. Key metrics include diagnostic accuracy against expert gold standards, the magnitude of judgment revision, confidence calibration, and cognitive load measured via the Paas Mental Effort Rating Scale. This design allows us to operationalize advice weighting and detect potential automation bias, the passive acceptance of AI output (Goddard et al., 2012), while analyzing variations across expertise and specialty. By moving beyond basic accuracy comparisons, this study provides an analysis of how GenAI advice is cognitively processed and integrated. The results will help inform the development of safer AI clinical tools, training protocols, and guidelines designed to maintain physicians’ analytical engagement while benefiting from AI consultation.

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