{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:47:48Z","timestamp":1791946068228,"version":"4.3.4"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Aust Endodontic J"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Large language models (LLMs) are increasingly used in healthcare, but their performance in endodontic decision\u2010making remains unclear. This study aimed to compare six LLMs in terms of diagnostic appropriateness for endodontic treatment planning. Fifty clinical scenarios were developed and entered into six LLMs (ChatGPT\u20104o, ChatGPT\u20103.5, Claude 4, Copilot, DeepSeek\u2010V3, Gemini 2.5). Two specialists scored responses as appropriate or inappropriate. Repeated measures ANOVA and chi\u2010square tests were used for analysis. Claude showed the highest accuracy (76%), followed by DeepSeek and Gemini. ChatGPT\u20103.5 had the lowest (40%). Significant differences were found between models (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05). Performance was better on straightforward cases than on complex scenarios. LLMs vary widely in diagnostic accuracy for endodontic cases. While some models show promise, others may provide confidently incorrect recommendations. Caution and human oversight remain essential until domain\u2010specific, fine\u2010tuned models are developed.\n                  <\/jats:p>","DOI":"10.1111\/aej.70040","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T09:36:53Z","timestamp":1763717813000},"page":"130-138","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Clinical Relevance of Large Language Models in Endodontics: Diagnostic Appropriateness Based on 50 Simulated Case Scenarios"],"prefix":"10.1111","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1262-7404","authenticated-orcid":false,"given":"B\u00fc\u015fra","family":"Karaca","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery, Faculty of Dentistry Burdur Mehmet Akif Ersoy University  Burdur Turkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4953-3772","authenticated-orcid":false,"given":"Yunus Emre","family":"\u00c7akmak","sequence":"additional","affiliation":[{"name":"Department of Endodontics, Faculty of Dentistry Akdeniz University  Antalya Turkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8319-6974","authenticated-orcid":false,"given":"Damla","family":"Erkal","sequence":"additional","affiliation":[{"name":"Department of Endodontics, Faculty of Dentistry Burdur Mehmet Akif Ersoy University  Burdur Turkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2019.04.014"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobcr.2020.07.015"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/0022034520969115"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joen.2021.06.003"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oraloncology.2020.104885"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6659133"},{"key":"e_1_2_9_8_1","article-title":"Comparing the Performance of ChatGPT 4o, DeepSeek R1, and Gemini 2 Pro in Answering Fixed Prosthodontics Questions Over Time","author":"Shirani M.","year":"2025","journal-title":"Journal of Prosthetic Dentistry"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1177\/0022034520915714"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02459-6"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11060887"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12909-025-06849-w"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.140204.1"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.5005\/jp-journals-10024-3465"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-025-02954-4"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7"},{"issue":"1","key":"e_1_2_9_17_1","article-title":"A Comparative Analysis of AI Models in Complex Medical Decision\u2010Making Scenarios: Evaluating ChatGPT, Claude AI, Bard, and Perplexity","volume":"16","author":"Uppalapati V. K.","year":"2024","journal-title":"Cureus"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-025-03726-3"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-025-03727-2"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sasc.2025.200396"},{"key":"e_1_2_9_21_1","volume-title":"Assessing Chatgpt's Potential in Endodontics: Preliminary Findings From A Diagnostic Accuracy Study","author":"Bragazzi N. 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