Evidence map›Paper›PMID 42213113›Full record

ArticleEuropean radiology2026

Evaluating cognitive biases in AI-assisted mammography interpretation: a simulation reader study of explainable AI across radiologist experience levels.

Filippo Pesapane, Antuono Latronico, Francesca Abbate, Silvia Penco, Anna Rotili, Valeria Dominelli, Luca Nicosia, Dario Monzani, Roberto Grasso, Gabriella Pravettoni and 1 more

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Filippo PesapaneBreast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy. filippo.pesapane@ieo.it.ORCID http://orcid.org/0000-0002-0374-5054
Antuono LatronicoBreast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy.
Francesca AbbateBreast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy.
Silvia PencoBreast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy.
Anna RotiliBreast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy.
Valeria DominelliBreast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy.
Luca NicosiaBreast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy.
Dario MonzaniDepartment of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, Italy.
Roberto GrassoApplied Research Division for Cognitive and Psychological Science, European Institute of Oncology, IRCCS, Milan, Italy.
Gabriella PravettoniApplied Research Division for Cognitive and Psychological Science, European Institute of Oncology, IRCCS, Milan, Italy.
Enrico CassanoBreast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo 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

methodsIn 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.

resultsIn 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.

conclusionAutomation 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.

Indexed as

Artificial IntelligenceBreast NeoplasmsClinical CompetenceMammographyRadiographic Image Interpretation, Computer-AssistedRadiologistsBiasComputer SimulationFemaleHumansObserver VariationArtificial intelligenceAutomation biasCognitive biasExplainable artificial intelligenceMammography

Identifiers

PMID42213113

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.