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Evaluating cognitive biases in AI-assisted mammography interpretation: a simulation reader study of explainable AI
Filippo Pesapane1, Antuono Latronico2, Francesca Abbate2
1Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy. filippo.pesapane@ieo.it.
Objectives:
To evaluate the impact of automation and anchoring bias in artificial intelligence (AI)-assisted mammography interpretation and to assess whether saliency-based explainable AI (XAI) mitigates these biases across radiologists of varying experience.
Materials And Methods:
In this monocentric, fully crossed simulation reader study conducted between March and June 2024, six breast radiologists stratified by experience independently reviewed 200 mammograms under three sequential conditions: unassisted, AI-assisted, and AI-assisted with saliency-based XAI heatmaps. To quantify susceptibility to misleading AI advice under controlled discordance conditions, BI-RADS-like AI recommendations were deliberately perturbed by one category in 30% of examinations, whereas the remaining 70% retained the native AI output. Bias outcomes were analyzed using generalized linear mixed-effects models accounting for reader- and case-level clustering.
Results:
In the AI-assisted condition without explanations, automation bias occurred in 65/180 (36.1%) and anchoring bias in 61/180 (33.9%) of manipulated cases. With XAI, these rates decreased to 32/180 (17.8%) and 31/180 (17.2%), respectively. In mixed-effects models, XAI was associated with lower odds of automation bias (aOR 0.56, 95% CI 0.44-0.71; p < 0.001) and anchoring-related revision bias (aOR 0.61, 95% CI 0.48-0.78; p < 0.001). On the non-manipulated subset, diagnostic accuracy improved from 724/840 (86.2%) in the unaided phase to 757/840 (90.1%) in the AI + XAI phase.
Conclusion:
Automation and anchoring bias affected AI-assisted mammography interpretation, particularly among less experienced radiologists. Saliency-based explainable AI reduced, but did not eliminate, these effects.
Key Points:
Question AI assistance can systematically influence BI-RADS decisions in mammography, particularly among less experienced radiologists, through automation and anchoring biases. Findings Saliency-based explainable AI (XAI) substantially reduces biased decisions while modestly improving overall diagnostic accuracy compared with standard AI support alone. Clinical relevance Embedding XAI and targeted training into AI-assisted mammography workflows may enhance patient safety and support safer clinical integration of mammography AI tools.
