TY - JOUR
AU - 
AB - www.nature.com/scientificreports OPEN The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination 1* 1 1 2 Mohammad H. Rezazade Mehrizi , Ferdinand Mol , Marcel Peter , Erik Ranschaert , 3 4 5 3 Daniel Pinto Dos Santos , Ramin Shahidi , Mansoor Fatehi & Thomas Dratsch Various studies have shown that medical professionals are prone to follow the incorrect suggestions offered by algorithms, especially when they have limited 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 shows 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 
TI - The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination
JF - Scientific Reports
DO - 10.1038/s41598-023-36435-3
DA - 2023-06-07
UR - https://www.deepdyve.com/lp/unpaywall/the-impact-of-ai-suggestions-on-radiologists-decisions-a-pilot-study-7RX9cn905r
DP - DeepDyve
ER -