{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T11:04:45Z","timestamp":1784545485949,"version":"3.55.0"},"reference-count":20,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Gynecology &amp;amp; Obste"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Large language models (LLMs) are increasingly utilized in modern medicine. In obstetrics, their ability to interpret complex imaging, such as ultrasound images for placenta accreta spectrum (PAS), remains largely unexplored. This study aimed to investigate the capacity of multimodal LLMs to interpret sonographic images of placentae and differentiate between normal findings and various types of PAS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In a comparative, two\u2010run, cross\u2010sectional design, three LLMs (Chat\u2010GPT, Google Gemini, and Claude) were tested using 18 cases of PAS and 18 normal placental ultrasound images (B mode and Doppler). Four clinical experts evaluated the models' performance, diagnostic accuracy, and justification of findings using standardized evaluation criteria. Statistical analyses, including Cochran's\n                      <jats:italic>Q<\/jats:italic>\n                      and McNemar's tests, were used to compare model performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Gemini and Chat\u2010GPT significantly outperformed Claude in diagnostic accuracy (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001). Gemini demonstrated the highest sensitivity (100%), followed by Chat\u2010GPT (88.9%), whereas Claude's sensitivity was notably low (5.6%). However, all models exhibited poor specificity, with Gemini reaching only 33.3% and both Chat\u2010GPT and Claude at nearly 0%, frequently misidentifying normal placentae as abnormal.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Gemini and Chat\u2010GPT show high sensitivity for detecting PAS but are limited by low specificity and a tendency to report non\u2010standard findings. Currently, LLMs cannot function as independent diagnostic tools in obstetric imaging for PAS but may serve as high\u2010sensitivity screening assistants for triage to specialized centers.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ijgo.71240","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:20:52Z","timestamp":1784542852000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The role of large language models as screening assistants in the diagnosis of placenta accreta spectrum pathologies"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8516-6242","authenticated-orcid":false,"given":"Iason","family":"Psilopatis","sequence":"first","affiliation":[{"name":"Division of Gynecologic Ultrasound and Prenatal Diagnostics, Department of Gynecology and Obstetrics University Hospital Basel  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heidrun","family":"Schoenberger","sequence":"additional","affiliation":[{"name":"Division of Gynecologic Ultrasound and Prenatal Diagnostics, Department of Gynecology and Obstetrics University Hospital Basel  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annkatrin","family":"Butenschoen","sequence":"additional","affiliation":[{"name":"Division of Gynecologic Ultrasound and Prenatal Diagnostics, Department of Gynecology and Obstetrics University Hospital Basel  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thabea K.","family":"Musik","sequence":"additional","affiliation":[{"name":"Division of Gynecologic Ultrasound and Prenatal Diagnostics, Department of Gynecology and Obstetrics University Hospital Basel  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgia","family":"Levidou","sequence":"additional","affiliation":[{"name":"Department of Pathology Nuremberg Clinic, Paracelsus Medical University  Nuremberg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beatrice","family":"Mosimann","sequence":"additional","affiliation":[{"name":"Division of Gynecologic Ultrasound and Prenatal Diagnostics, Department of Gynecology and Obstetrics University Hospital Basel  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gwendolin","family":"Manegold\u2010Brauer","sequence":"additional","affiliation":[{"name":"Division of Gynecologic Ultrasound and Prenatal Diagnostics, Department of Gynecology and Obstetrics University Hospital Basel  Basel Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocad258"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.2196\/66691"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s44387-025-00047-1"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2025.1530799"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.4258\/hir.2025.31.2.114"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00404-025-08065-9"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2023.03.009"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijgo.15869"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2025.03.026"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.2147\/IJWH.S395271"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejogrb.2021.02.001"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/FM9.0000000000000142"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2025.21909"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijgo.14595"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/aogs.14943"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.26155"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1572-0241.2007.01325.x"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2024.07.007"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-025-01486-2"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-025-01578-3"}],"container-title":["International Journal of Gynecology &amp; 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