Mitigating Automation Bias in Physician-LLM Diagnostic Reasoning Using Behavioral Nudges

The goal of this randomized controlled trial is to evaluate whether behavioral nudges can reduce automation bias, the uncritical acceptance of automated output, in physicians using large language models (LLM) like ChatGPT-5.1 for clinical decision-making. The main question it aims to answer is: Does a dual-mechanism behavioral nudge intervention (baseline accuracy anchoring plus case-specific color-coded confidence signals) reduce physicians' uncritical acceptance of incorrect LLM recommendations? Researchers will compare physicians who receive LLM recommendations along with a behavioral nudge to those who receive LLM recommendations without the nudge to assess if the nudge reduces automation bias. Participants will: * Evaluate six clinical vignettes accompanied by LLM-generated recommendations (half containing deliberate, clinically significant errors). * Control group: Be able to view LLM recommendations in standard format without the nudge. * Treatment group: Be able to view ChatGPT's diagnostic accuracy on standard medical datasets as an initial anchor, then receive color-coded confidence signals alongside each recommendation (e.g., red for low confidence). * Have their responses evaluated by blinded reviewers using an expert-developed assessment rubric to detect uncritical acceptance of erroneous information.