{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T10:07:09Z","timestamp":1774951629942,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T00:00:00Z","timestamp":1774742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Background\/Objectives: Breast cancer screening such as mammography supports earlier detection, but variability in interpretation can still lead to missed cancers and avoidable follow-up testing. We evaluated ChatGPT-5.2 Thinking (OpenAI) as a stand-alone model for examination-level malignancy classification on standard bilateral mammography views in a biopsy-referred cohort, compared with breast radiologists, and assessed laterality performance. Methods: We conducted a retrospective, multicenter diagnostic-accuracy study across breast imaging centers in Saudi Arabia. From an upstream screened cohort (n = 1225), we constructed a biopsy-referred test set of 100 mammography examinations (four 2D views per exam: bilateral CC and MLO; 400 images), including 61 biopsy-confirmed malignancies and 39 biopsy-negative controls, with pathology as the reference standard. Radiologists were blinded to pathology and AI outputs and assigned BI-RADS (0\u20135) and suspected laterality. ChatGPT-5.2 interpreted the same de-identified views using a BI-RADS-guided prompt to generate BI-RADS and laterality. The sensitivity, specificity, accuracy, and laterality concordance were then estimated. Results: ChatGPT-5.2 had higher sensitivity than radiologists (95.08% vs. 81.97%) but markedly lower specificity (10.26% vs. 56.41%), resulting in lower overall accuracy (62.00% vs. 72.00%). The AI produced 58 true positives, 35 false positives, and 3 false negatives, while radiologists produced 50 true positives, 17 false positives, and 11 false negatives. Laterality accuracy among malignant examinations was 60.66%. Conclusions: In this pathology-anchored, biopsy-referred evaluation, ChatGPT-5.2 identified more cancers but generated substantially more false-positive classifications and showed only moderate breast-side localization. These findings support use as a concurrent aid or prioritization tool rather than a stand-alone reader and motivate efforts to improve specificity and laterality before prospective validation.<\/jats:p>","DOI":"10.3390\/tomography12040045","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T13:40:52Z","timestamp":1774878052000},"page":"45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical Performance Tradeoffs of ChatGPT-5.2 Thinking (OpenAI) Compared with Radiologist Interpretation in Biopsy-Referred Mammography: Cancer Detection, False Positives, and Laterality"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8269-2367","authenticated-orcid":false,"given":"Mohammad","family":"Alarifi","sequence":"first","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia"},{"name":"Visiting Scholar, Department of Health Informatics & Administration, University of Wisconsin\u2013Milwaukee, Milwaukee, WI 53201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6646-6378","authenticated-orcid":false,"given":"Areej","family":"Aloufi","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1397-582X","authenticated-orcid":false,"given":"Abdulrahman","family":"Jabour","sequence":"additional","affiliation":[{"name":"Department of Public Health, Faculty of Nursing and Health Science, Jazan University, P.O. Box 114, Jazan 45142, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9029-0368","authenticated-orcid":false,"given":"Ahmad","family":"Abanomy","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5614-5063","authenticated-orcid":false,"given":"Haitham","family":"Alahmad","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2858-4759","authenticated-orcid":false,"given":"Khaled","family":"Alenazi","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8607-1338","authenticated-orcid":false,"given":"Alhanouf","family":"Alshedi","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3092-4795","authenticated-orcid":false,"given":"Mansour","family":"Almanaa","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,29]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"e230149","DOI":"10.1148\/rycan.230149","article-title":"AI-enhanced mammography with digital breast tomosynthesis for breast cancer detection: Clinical value and comparison with human performance","volume":"6","author":"Resch","year":"2024","journal-title":"Radiol. Imaging Cancer"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"e230318","DOI":"10.1148\/ryai.230318","article-title":"Impact of AI for digital breast tomosynthesis on breast cancer detection and interpretation time","volume":"6","author":"Park","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.radi.2021.04.002","article-title":"Breast cancer detection: Comparison of digital mammography and digital breast tomosynthesis across non-dense and dense breasts","volume":"27","author":"Hadadi","year":"2021","journal-title":"Radiography"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1148\/radiol.210948","article-title":"An artificial intelligence\u2013based mammography screening protocol for breast cancer: Outcome and radiologist workload","volume":"304","author":"Lauritzen","year":"2022","journal-title":"Radiology"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1093\/jnci\/djy222","article-title":"Stand-alone artificial intelligence for breast cancer detection in mammography: Comparison with 101 radiologists","volume":"111","author":"Broeders","year":"2019","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Choi, W.J., An, J.K., Woo, J.J., and Kwak, H.Y. (2022). Comparison of diagnostic performance in mammography assessment: Radiologist with reference to clinical information versus standalone artificial intelligence detection. Diagnostics, 13.","DOI":"10.3390\/diagnostics13010117"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100545","DOI":"10.1016\/j.ejro.2023.100545","article-title":"Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography","volume":"12","author":"Lee","year":"2024","journal-title":"Eur. J. Radiol. Open"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e190208","DOI":"10.1148\/ryai.2020190208","article-title":"Improving breast cancer detection accuracy of mammography with the concurrent use of an artificial intelligence tool","volume":"2","author":"Lopez","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e220146","DOI":"10.1148\/ryai.220146","article-title":"Impact of different mammography systems on artificial intelligence performance in breast cancer screening","volume":"5","author":"Colosimo","year":"2023","journal-title":"Radiol. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.3348\/kjr.2022.0263","article-title":"Use of artificial intelligence for reducing unnecessary recalls at screening mammography: A simulation study","volume":"23","author":"Kim","year":"2022","journal-title":"Korean J. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.jacr.2021.11.008","article-title":"Independent external validation of artificial intelligence algorithms for automated interpretation of screening mammography: A systematic review","volume":"19","author":"Anderson","year":"2022","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1186\/s43055-024-01353-5","article-title":"Artificial intelligence as an initial reader for double reading in breast cancer screening: A prospective initial study of 32,822 mammograms of the Egyptian population","volume":"55","author":"Mansour","year":"2024","journal-title":"Egypt. J. Radiol. Nucl. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"505","DOI":"10.3348\/kjr.2021.0476","article-title":"Improving the performance of radiologists using artificial intelligence-based detection support software for mammography: A multi-reader study","volume":"23","author":"Lee","year":"2022","journal-title":"Korean J. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e232303","DOI":"10.1148\/radiol.232303","article-title":"Accuracy of an artificial intelligence system for interval breast cancer detection at screening mammography","volume":"312","author":"Nanaa","year":"2024","journal-title":"Radiology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e230375","DOI":"10.1148\/ryai.230375","article-title":"Performance of an artificial intelligence system for breast cancer detection on screening mammograms from BreastScreen Norway","volume":"6","author":"Larsen","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5940","DOI":"10.1007\/s00330-021-07686-3","article-title":"Can artificial intelligence reduce the interval cancer rate in mammography screening?","volume":"31","author":"Hofvind","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8238","DOI":"10.1007\/s00330-022-08909-x","article-title":"Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations","volume":"32","author":"Larsen","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1093\/bjr\/tqaf222","article-title":"Radiologists\u2019 Views on AI and the Future of Radiology: Insights from a US National Survey","volume":"99","author":"Alarifi","year":"2025","journal-title":"Br. J. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10549-024-07353-3","article-title":"Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: A systematic review","volume":"207","author":"Zeng","year":"2024","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_23","first-page":"e79353","article-title":"Screening Mammography and Artificial Intelligence: A Comprehensive Systematic Review","volume":"17","author":"Abeelh","year":"2025","journal-title":"Cureus"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1007\/s00330-020-07165-1","article-title":"Identifying normal mammograms in a large screening population using artificial intelligence","volume":"31","author":"Dustler","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4324","DOI":"10.1007\/s10278-025-01454-1","article-title":"Appropriateness of Thyroid Nodule Cancer Risk Assessment and Management Recommendations Provided by Large Language Models","volume":"38","author":"Alarifi","year":"2025","journal-title":"J. Imaging Inform. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s11604-022-01327-5","article-title":"Comparisons between artificial intelligence computer-aided detection synthesized mammograms and digital mammograms when used alone and in combination with tomosynthesis images in a virtual screening setting","volume":"41","author":"Uematsu","year":"2023","journal-title":"Jpn. J. Radiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e2329655","DOI":"10.2214\/AJR.23.29655","article-title":"Screening in patients with dense breasts: Comparison of mammography, artificial intelligence, and supplementary ultrasound","volume":"222","author":"Lee","year":"2024","journal-title":"Am. J. Roentgenol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1862","DOI":"10.1016\/j.jacr.2024.08.006","article-title":"Patient, referring physician, and radiologist opinions over time on providing patients access to radiology reports: A systematic review","volume":"21","author":"Alarifi","year":"2024","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_29","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":"The Lancet Oncology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1148\/radiol.211590","article-title":"Stand-alone use of artificial intelligence for digital mammography and digital breast tomosynthesis screening: A retrospective evaluation","volume":"302","year":"2022","journal-title":"Radiology"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s10278-019-00192-5","article-title":"Improved cancer detection using artificial intelligence: A retrospective evaluation of missed cancers on mammography","volume":"32","author":"Watanabe","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Alarifi, M. (2025). Exploring Radiologists\u2019 Use of AI Chatbots for Assistance in Image Interpretation: Patterns of Use and Trust Evaluation. J. Imaging Inform. Med., 1\u201310.","DOI":"10.1007\/s10278-025-01640-1"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1093\/jnci\/djaa205","article-title":"Performance of digital breast tomosynthesis, synthetic mammography, and digital mammography in breast cancer screening: A systematic review and meta-analysis","volume":"113","author":"Alabousi","year":"2021","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"587","DOI":"10.2214\/AJR.20.23976","article-title":"Screening mammography performance metrics of 2D digital mammography versus digital breast tomosynthesis in women with a personal history of breast cancer","volume":"217","author":"Chikarmane","year":"2021","journal-title":"Am. J. Roentgenol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e468","DOI":"10.1016\/S2589-7500(20)30185-0","article-title":"Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: A retrospective simulation study","volume":"2","author":"Dembrower","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4825","DOI":"10.1007\/s00330-019-06186-9","article-title":"Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study","volume":"29","author":"Teuwen","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1007\/s12282-020-01061-8","article-title":"Artificial intelligence for breast cancer detection in mammography: Experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women","volume":"27","author":"Sasaki","year":"2020","journal-title":"Breast Cancer"},{"key":"ref_39","first-page":"1","article-title":"Breast cancer detection across dense and non-dense breasts: Markers of diagnostic confidence and efficacy","volume":"11","author":"Hadadi","year":"2022","journal-title":"Acta Radiol. Open"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1148\/radiol.2251011667","article-title":"Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: An analysis of 27,825 patient evaluations","volume":"225","author":"Kolb","year":"2002","journal-title":"Radiology"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e222639","DOI":"10.1148\/radiol.222639","article-title":"Standalone AI for breast cancer detection at screening digital mammography and digital breast tomosynthesis: A systematic review and meta-analysis","volume":"307","author":"Yoon","year":"2023","journal-title":"Radiology"},{"key":"ref_42","first-page":"552","article-title":"Artificial intelligence\u2019s transformative role in management of electronic medical records","volume":"5","author":"Chughtai","year":"2024","journal-title":"J. Pak. Soc. Intern. Med."}],"container-title":["Tomography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2379-139X\/12\/4\/45\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:19:01Z","timestamp":1774948741000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2379-139X\/12\/4\/45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,29]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["tomography12040045"],"URL":"https:\/\/doi.org\/10.3390\/tomography12040045","relation":{},"ISSN":["2379-139X"],"issn-type":[{"value":"2379-139X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,29]]}}}