The Influence of Large Language Model-Generated Artificial Intelligence Risk Estimates on Clinical Decision-Making in...

Background/Objectives: Large language models (LLMs) are increasingly used as decision-support tools in radiology, yet their influence on clinical decision-making remains a significant concern. This study evaluates how artificial intelligence (AI)-generated malignancy risk estimates influence clinical decisions in breast imaging and assesses automation bias (AB) among radiology residents at different training levels. Methods: In this simulation-based study, 20 residents reviewed radiology reports for 15 synthetic breast imaging cases generated by ChatGPT-4 Turbo, each presented in either a concordant or discordant format. Participants were stratified into two groups based on their completion of a breast imaging rotation and evaluated each case by assigning a BI-RADS score, recommending management, and rating their confidence in the AI risk estimate. Diagnostic accuracy and independent predictors of AB were analyzed using Generalized Estimating Equations (GEE) to account for reader-level clustering. Results: Overall management accuracy was significantly lower in cases with discordant AI outputs (p = 0.008). A similar, albeit non-significant, decreasing trend was observed for BI-RADS accuracy. While advanced residents maintained high management accuracy regardless of AI concordance, early-stage residents showed a sharp decline in discordant cases (77.3% vs. 45.3%; p = 0.006). This decline was most critical in malignant cases for early-stage residents, where accuracy dropped from 92.0% to 48.0% (p < 0.001). In the multivariable GEE logistic regression model, training level was the only independent predictor of AB (OR: 0.09, 95% CI: 0.03-0.28, p < 0.001). Conclusions: In this simulation study, advanced clinical training was associated with lower susceptibility to AB in breast imaging, although training level alone is unlikely to be a sufficient safeguard. Safe integration of AI tools will require caution among junior trainees alongside system-level measures such as model transparency and institutional oversight.