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

Automation bias represents a critical challenge in modern clinical practice, particularly as artificial intelligence (AI) tools become increasingly embedded in healthcare workflows. This cognitive phenomenon describes the tendency of clinicians to favor suggestions from automated decision-making systems, even when those suggestions are incorrect. As Large Language Models (LLM) such as ChatGPT-5.1 gain traction in medical settings, their potential to reduce errors and improve efficiency must be weighed against a significant concern: these models lack rigorous medical validation and may amplify existing cognitive biases through incorrect or misleading recommendations.

The emergence of automation bias in medical contexts reflects a complex interplay of environmental and psychological factors. Time constraints in high-volume clinical settings create pressure to accept AI-generated recommendations without adequate scrutiny. Financial incentives that prioritize efficiency over thoroughness may further discourage critical evaluation necessary for sound clinical judgment. Cognitive fatigue during extended shifts diminishes physicians' capacity for sustained analytical thinking. These pressures interact with psychological mechanisms including diffusion of responsibility, overconfidence in technological solutions, and cognitive offloading, collectively creating conditions where uncritical acceptance of AI-generated recommendations becomes more likely.

This randomized controlled trial evaluates the effectiveness of a behavioral nudge intervention designed to mitigate automation bias among medical doctors utilizing LLM-generated diagnostic recommendations. The primary objective is to determine whether this intervention improves diagnostic reasoning performance scores when evaluating clinical vignettes that include deliberately flawed LLM recommendations. Secondary objectives include assessing whether physician experience level, gender, and prior LLM experience moderate the intervention's effectiveness, determining differential effectiveness for vignettes across different confidence signals.

This study employs a single-blind, randomized controlled trial with two parallel arms. Participants will be randomly assigned 1:1 to either the intervention or control arm. To eliminate variability from differences in prompting skills, participants will not interact directly with a live LLM interface. Instead, all participants will use a custom-built web platform displaying clinical vignettes with pre-generated LLM recommendations, ensuring identical LLM-generated content for each vignette.

All participants will evaluate six clinical vignettes during a single, proctored session lasting approximately 75 minutes. Three vignettes will contain deliberately introduced clinical reasoning flaws in the LLM recommendations, while three will contain correct recommendations. Vignettes will be presented in randomized order to prevent pattern detection.