How are radiologists' decisions impacted by AI suggestions? Moderating effect of explainability inputs and attitudinal priming in examining mammograms

Abstract

Abstract Various studies have shown that medical professionals are prone to follow the incorrect suggestions offered by algorithms, especially when they have limited informational inputs to interrogate and interpret such suggestions and when they have an attitude of relying on them. We examine the effect of correct and incorrect algorithmic suggestions on the diagnosis performance of radiologists when 1) they have no, partial, and extensive informational inputs for explaining the suggestions (study 1) and 2) they are primed to hold a positive, negative, ambivalent, or neutral attitude towards AI (study 2). Our analysis of 2760 decisions made by 92 radiologists conducting 15 mammography examinations show that radiologists' diagnoses follow both incorrect and correct suggestions, despite variations in the explainability inputs and attitudinal priming interventions. We identify and explain various pathways through which radiologists navigate through the decision process and arrive at correct or incorrect decisions. Overall, the findings of both studies show the limited effect of using explainability inputs and attitudinal priming for overcoming the unintended influence of (incorrect) algorithmic suggestions.