Facilitating Trust Calibration in Artificial Intelligence–Driven Diagnostic Decision Support Systems for Determining Physicians’ Diagnostic Accuracy: Quasi-Experimental Study

Abstract

Abstract BackgroundDiagnostic errors are significant problems in medical care. Despite the usefulness of artificial intelligence (AI)–based diagnostic decision support systems, the overreliance of physicians on AI-generated diagnoses may lead to diagnostic errors. ObjectiveWe investigated the safe use of AI-based diagnostic decision support systems with trust calibration by adjusting trust levels to match the actual reliability of AI. MethodsA quasi-experimental study was conducted at Dokkyo Medical University, Japan, with physicians allocated (1:1) to the intervention and control groups. A total of 20 clinical cases were created based on the medical histories recorded by an AI-driven automated medical history–taking system from actual patients who visited a community-based hospital in Japan. The participants reviewed the medical histories of 20 clinical cases generated by an AI-driven automated medical history–taking system with an AI-generated list of 10 differential diagnoses and provided 1 to 3 possible diagnoses. Physicians were asked whether the final diagnosis was in the AI-generated list of 10 differential diagnoses in the intervention group, which served as the trust calibration. We analyzed the diagnostic accuracy of physicians and the correctness of the trust calibration in the intervention group. We also investigated the relationship between the accuracy of the trust calibration and the diagnostic accuracy of physicians, and the physicians’ confidence level regarding the use of AI. ResultsAmong the 20 physicians assigned to the intervention (n=10) and control (n=10) groups, the mean age was 30.9 (SD 3.9) years and 31.7 (SD 4.2) years, the proportion of men was 80% and 60%, and the mean postgraduate year was 5.8 (SD 2.9) and 7.2 (SD 4.6), respectively, with no significant differences. The physicians’ diagnostic accuracy was 41.5% in the intervention group and 46% in the control group, with no significant difference (95% CI −0.75 to 2.55; PP ConclusionsTrust calibration did not significantly improve physicians’ diagnostic accuracy when considering the differential diagnoses generated by reading medical histories and the possible differential diagnosis lists of an AI-driven automated medical history–taking system. As this was a formative study, the small sample size and suboptimal trust calibration methods may have contributed to the lack of significant differences. This study highlights the need for a larger sample size and the implementation of supportive measures of trust calibration.