{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T23:24:41Z","timestamp":1791588281646,"version":"4.3.4"},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Background: Artificial intelligence (AI) is increasingly used as a decision-support tool in mammography, but its influence on radiology residents\u2019 interpretive behavior remains insufficiently characterized. This study evaluated the impact of AI assistance on BI-RADS classification and diagnostic confidence among radiology residents. Methods: This retrospective, single-center, multi-reader paired study included 112 diagnostic mammography examinations, corresponding to 223 assessable breasts and 2230 resident-breast readings. Ten radiology residents interpreted 2D mammography and digital breast tomosynthesis examinations first without AI assistance and subsequently with access to AI output. Changes in the BI-RADS category, diagnostic confidence, reasons for modification, and agreement with an expert-consensus BI-RADS reference standard were analyzed. Results: AI-assisted reassessment changed the BI-RADS classification in 9.7% of readings and diagnostic confidence in 19.2%, with any AI-associated modification observed in 24.3% of instances. Upgrades were more frequent than downgrades, particularly for medium- and high-suspicion AI outputs. Confidence increased more often than it decreased. Expert-reference agreement improved modestly, and BI-RADS 4+ sensitivity increased from 72.0% to 82.3%, with stable negative agreement, but these metrics reflect agreement with expert BI-RADS consensus rather than pathology-confirmed cancer detection. Conclusions: AI assistance influenced both BI-RADS reassessment and diagnostic confidence among radiology residents, producing modest but directionally favorable changes. These findings support cautious, supervised integration of AI into breast imaging training, with attention to confidence calibration and potential overreliance.<\/jats:p>","DOI":"10.3390\/life16061020","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T15:11:22Z","timestamp":1781709082000},"page":"1020","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The Impact of Artificial Intelligence on BI-RADS Classification and Diagnostic Confidence in Mammography Interpretation by Radiology Residents"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3099-4637","authenticated-orcid":false,"given":"Ioana-Andreea","family":"C\u00eerlig","sequence":"first","affiliation":[{"name":"Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandru-Marian","family":"Olaru","sequence":"additional","affiliation":[{"name":"Department of Radiology and Medical Imaging, Emergency Clinical County Hospital of Craiova, 200642 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3650-5037","authenticated-orcid":false,"given":"Mihai-Alexandru","family":"Ene","sequence":"additional","affiliation":[{"name":"Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aurelia-\u0218tefania","family":"Domenco","sequence":"additional","affiliation":[{"name":"Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9147-7951","authenticated-orcid":false,"given":"Rossy-Vl\u0103du\u021b","family":"Teic\u0103","sequence":"additional","affiliation":[{"name":"Department of Radiology and Medical Imaging, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina-Mihaela","family":"Ciofiac","sequence":"additional","affiliation":[{"name":"Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"},{"name":"Department of Radiology and Medical Imaging, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1642-3587","authenticated-orcid":false,"given":"Raluca-Elena","family":"Nica","sequence":"additional","affiliation":[{"name":"Department of Radiology and Medical Imaging, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Violeta-Maria","family":"Novac","sequence":"additional","affiliation":[{"name":"Filantropia Municipal Clinical Hospital, 200143 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1524-2494","authenticated-orcid":false,"given":"M\u0103d\u0103lin","family":"M\u0103muleanu","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Electronics, University of Craiova, 200585 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucian-Mihai","family":"Florescu","sequence":"additional","affiliation":[{"name":"Department of Radiology and Medical Imaging, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0954-1266","authenticated-orcid":false,"given":"Ioana-Andreea","family":"Gheonea","sequence":"additional","affiliation":[{"name":"Department of Radiology and Medical Imaging, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.acra.2016.09.010","article-title":"Inter-reader Variability in the Use of BI-RADS Descriptors for Suspicious Findings on Diagnostic Mammography: A Multi-institution Study of 10 Academic Radiologists","volume":"24","author":"Lee","year":"2017","journal-title":"Acad. Radiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1038\/sj.bjc.6604954","article-title":"Inter-observer variability in mammography screening and effect of type and number of readers on screening outcome","volume":"100","author":"Duijm","year":"2009","journal-title":"Br. J. Cancer"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.2214\/AJR.09.3735","article-title":"The future of mammography: Radiology residents\u2019 experiences, attitudes, and opinions","volume":"194","author":"Baxi","year":"2010","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1148\/radiol.2533090070","article-title":"When Radiologists Perform Best: The Learning Curve in Screening Mammogram Interpretation","volume":"253","author":"Miglioretti","year":"2009","journal-title":"Radiology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/S1076-6332(99)80252-9","article-title":"How experience and training influence mammography expertise","volume":"6","author":"Nodine","year":"1999","journal-title":"Acad. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/S0140-6736(25)02464-X","article-title":"Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: A randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial","volume":"407","author":"Gommers","year":"2026","journal-title":"Lancet"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1016\/S1470-2045(23)00298-X","article-title":"Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): A clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study","volume":"24","author":"Josefsson","year":"2023","journal-title":"Lancet Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e138","DOI":"10.1016\/S2589-7500(20)30003-0","article-title":"Changes in cancer detection and false-positive recall in mammography using artificial intelligence: A retrospective, multireader study","volume":"2","author":"Kim","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1038\/s41598-025-31065-3","article-title":"AI assistance improves reader agreement in digital mammography: A multireader crossover study of general and breast subspecialty radiologists","volume":"16","author":"Ahmadzade","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e66931","DOI":"10.2196\/66931","article-title":"Impact of AI on Breast Cancer Detection Rates in Mammography by Radiologists of Varying Experience Levels in Singapore: Preliminary Comparative Study","volume":"9","author":"Goh","year":"2025","journal-title":"JMIR Form. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5016","DOI":"10.1016\/j.acra.2025.05.025","article-title":"Clinical Application of AI in Mammography: Insights from a Prospective Study","volume":"32","author":"Yilmaz","year":"2025","journal-title":"Acad. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s12282-022-01375-9","article-title":"Impact of artificial intelligence in breast cancer screening with mammography","volume":"29","author":"Dang","year":"2022","journal-title":"Breast Cancer"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8682","DOI":"10.1007\/s00330-021-07992-w","article-title":"Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: A multi-reader multi-case study","volume":"31","author":"Appelman","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"79480","DOI":"10.1109\/ACCESS.2023.3298569","article-title":"BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations","volume":"11","author":"Zhang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"20210820","DOI":"10.1259\/bjr.20210820","article-title":"Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?","volume":"94","author":"Mansour","year":"2021","journal-title":"Br. J. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e70924","DOI":"10.2196\/70924","article-title":"Using a Large Language Model for Breast Imaging Reporting and Data System Classification and Malignancy Prediction to Enhance Breast Ultrasound Diagnosis: Retrospective Study","volume":"13","author":"Miaojiao","year":"2025","journal-title":"JMIR Med. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1136\/amiajnl-2011-000089","article-title":"Automation bias: A systematic review of frequency, effect mediators, and mitigators","volume":"19","author":"Goddard","year":"2011","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1001\/jama.2023.22557","article-title":"Automation Bias and Assistive AI: Risk of Harm From AI-Driven Clinical Decision Support","volume":"330","author":"Khera","year":"2023","journal-title":"JAMA"},{"key":"ref_19","first-page":"460","article-title":"Exploring the risks of automation bias in healthcare artificial intelligence applications: A Bowtie analysis","volume":"5","author":"Abdelwanis","year":"2024","journal-title":"J. Saf. Sci. Resil."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1148\/radiol.2018181371","article-title":"Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System","volume":"290","author":"Krupinski","year":"2019","journal-title":"Radiology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e222176","DOI":"10.1148\/radiol.222176","article-title":"Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance","volume":"307","author":"Dratsch","year":"2023","journal-title":"Radiology"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.2174\/1573405618666220422132251","article-title":"A Comparison of the Performances of Artificial Intelligence System and Radiologists in the Ultrasound Diagnosis of Thyroid Nodules","volume":"18","author":"He","year":"2022","journal-title":"Curr. Med. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Meetschen, M., Salh\u00f6fer, L., Beck, N., Kroll, L., Ziegenfu\u00df, C.D., Schaarschmidt, B.M., Forsting, M., Mizan, S., Umutlu, L., and Hosch, R. (2024). AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients. Diagnostics, 14.","DOI":"10.3390\/diagnostics14060596"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e2476","DOI":"10.7717\/peerj-cs.2476","article-title":"Diagnostic test accuracy of AI-assisted mammography for breast imaging: A narrative review","volume":"11","author":"Dave","year":"2025","journal-title":"PeerJ Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Retson, T.A., and Eghtedari, M. (2023). Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning\u2019s Role in Breast Imaging beyond Screening Mammography. Diagnostics, 13.","DOI":"10.3390\/diagnostics13132133"}],"container-title":["Life"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1729\/16\/6\/1020\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T04:35:09Z","timestamp":1782189309000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1729\/16\/6\/1020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,17]]},"references-count":25,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["life16061020"],"URL":"https:\/\/doi.org\/10.3390\/life16061020","relation":{},"ISSN":["2075-1729"],"issn-type":[{"value":"2075-1729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,17]]}}}