{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:03:02Z","timestamp":1786122182978,"version":"build-2736575974"},"reference-count":31,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia"},{"id":[{"id":"https:\/\/ror.org\/02f81g417","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Breast imaging reports contain Breast Imaging Reporting and Data System (BI-RADS) assessments and management recommendations that may be difficult for patients to understand across languages. Large language models (LLMs) may support patient-facing communication, but clinically important details must be preserved. This study compared radiologists\u2019 opinions regarding the quality, clinical fidelity, safety of wording, and communication usefulness of patient-friendly Arabic translations of BI-RADS breast imaging reports generated by three LLMs. Methods: Five de-identified reports representing BI-RADS categories 0, 2, 3, 4, and 6 were translated from English into Arabic by DeepSeek, ChatGPT, and Gemini using an identical structured prompt. Fifty radiologists rated the blinded outputs across eight 5-point domains. Model ratings were compared using Friedman tests, Kendall\u2019s W, and multiplicity-adjusted Wilcoxon signed-rank tests. Laterality was also verified against the source reports. Results: Gemini achieved the highest overall mean score (3.73 \u00b1 0.78), followed by DeepSeek (3.54 \u00b1 0.73) and ChatGPT (3.03 \u00b1 0.70). The overall model effect was significant (\u03c72 = 34.11, df = 2, p &lt; 0.001; Kendall\u2019s W = 0.341). Gemini and DeepSeek each outperformed ChatGPT across all eight domains (adjusted p &lt; 0.001), and Gemini outperformed DeepSeek overall (adjusted p = 0.009). No laterality errors were identified among the 15 translations. Conclusions: Performance remained model-dependent despite the shared prompt. Among the participating radiologists, Gemini received the highest expert ratings, while DeepSeek remained competitive. Because errors involving BI-RADS categories, laterality, measurements, lesion location, or recommendations could change diagnostic understanding, LLM-generated Arabic translations should serve as radiologist-reviewed communication aids rather than autonomous substitutes for clinical explanation.<\/jats:p>","DOI":"10.3390\/diagnostics16162495","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T09:14:28Z","timestamp":1786094068000},"page":"2495","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large Language Model Translation of BI-RADS Breast Imaging Reports into Arabic: A Blinded Expert Evaluation of Diagnostic Communication Safety"],"prefix":"10.3390","volume":"16","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, Riyadh 4545, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3900-643X","authenticated-orcid":false,"given":"Jake","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Health Sciences, University of Wisconsin\u2013Milwaukee, Milwaukee, WI 53211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1397-582X","authenticated-orcid":false,"given":"Abdulrahman","family":"Jabour","sequence":"additional","affiliation":[{"name":"Health Informatics Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan 45142, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0857-524X","authenticated-orcid":false,"given":"Yazeed","family":"Alashban","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, Riyadh 4545, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5922-6549","authenticated-orcid":false,"given":"Meaad","family":"Almusined","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, King Saud University, Riyadh 4545, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0605-0140","authenticated-orcid":false,"given":"Maram","family":"Mobara","sequence":"additional","affiliation":[{"name":"Radiology Department, Prince Sultan Military Medical City, Riyadh 12233, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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, Riyadh 4545, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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, Riyadh 4545, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alarifi, M., Patrick, T., Jabour, A., Wu, M., and Luo, J. 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