Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI.

Abstract

Background It is unclear whether artificial intelligence (AI) explanations help or hurt radiologists and other physicians in AI-assisted radiologic diagnostic decision-making. Purpose To test whether the type of AI explanation and the correctness and confidence level of AI advice impact physician diagnostic performance, perception of AI advice usefulness, and trust in AI advice for chest radiograph diagnosis. Materials and Methods A multicenter, prospective randomized study was conducted from April 2022 to September 2022. Two types of AI explanations prevalent in medical imaging-local (feature-based) explanations and global (prototype-based) explanations-were a between-participant factor, while AI correctness and confidence were within-participant factors. Radiologists (task experts) and internal or emergency medicine physicians (task nonexperts) received a chest radiograph to read; then, simulated AI advice was presented. Generalized linear mixed-effects models were used to analyze the effects of the experimental variables on diagnostic accuracy, efficiency, physician perception of AI usefulness, and "simple trust" (ie, speed of alignment with or divergence from AI advice); the control variables included knowledge of AI, demographic characteristics, and task expertise. Holm-Sidak corrections were used to adjust for multiple comparisons. Results Data from 220 physicians (median age, 30 years [IQR, 28-32.75 years]; 146 male participants) were analyzed. Compared with global AI explanations, local AI explanations yielded better physician diagnostic accuracy when the AI advice was correct (β = 0.86; P value adjusted for multiple comparisons [P_adj] < .001) and increased diagnostic efficiency overall by reducing the time spent considering AI advice (β = -0.19; P_adj = .01). While there were interaction effects of explanation type, AI confidence level, and physician task expertise on diagnostic accuracy (β = -1.05; P_adj = .04), there was no evidence that AI explanation type or AI confidence level significantly affected subjective measures (physician diagnostic confidence and perception of AI usefulness). Finally, radiologists and nonradiologists placed greater simple trust in local AI explanations than in global explanations, regardless of the correctness of the AI advice (β = 1.32; P_adj = .048). Conclusion The type of AI explanation impacted physician diagnostic performance and trust in AI, even when physicians themselves were not aware of such effects. © RSNA, 2024 Supplemental material is available for this article.