{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T20:10:17Z","timestamp":1790367017401,"version":"4.1.0"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T00:00:00Z","timestamp":1781308800000},"content-version":"vor","delay-in-days":163,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":["Emergency Medicine International"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Acute myocardial infarction (AMI) is a major global cause of morbidity and mortality. Large language models (LLMs) are emerging tools for patient education. This study evaluated the performance of three LLMs in delivering accurate, reliable, and readable educational content regarding AMI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this cross\u2010sectional study (February\u2013March 2025), a clinical case of a patient with an inferior STEMI ECG was presented to three LLMs: ChatGPT\u20104o, Claude 3.7 Sonnet, and Gemini Advanced 2.0 Flash. Each model answered 30 patient\u2010focused questions across three domains: general disease knowledge, diagnostic processes, and treatment approaches. Responses were assessed by four emergency medicine associate professors (10\u201320\u2009years of experience) using a 5\u2010point Likert scale for accuracy, DISCERN and EQIP tools for reliability and quality, and standard readability indices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      ChatGPT\u20104o achieved the highest accuracy score (4.38\u2009\u00b1\u20090.38), followed by Claude 3.7 (4.09\u2009\u00b1\u20090.55) and Gemini 2.0 (3.92\u2009\u00b1\u20090.41) (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001). ChatGPT\u20104o performed significantly better in general information (\n                      <jats:italic>p<\/jats:italic>\n                      = 0.002) and diagnostics (\n                      <jats:italic>p<\/jats:italic>\n                      = 0.009), while Claude 3.7 excelled in treatment\u2010related content (\n                      <jats:italic>p<\/jats:italic>\n                      = 0.015). Claude 3.7 produced significantly more readable responses than both ChatGPT\u20104o and Gemini 2.0 across all indices (Flesch\u2013Kincaid,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.002, Gunning Fog,\n                      <jats:italic>p<\/jats:italic>\n                      \u2264 0.001, Coleman\u2013Liau,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.003). ChatGPT\u20104o scored \u201cexcellent\u201d on the DISCERN scale; all models were rated as \u201cgood quality with minor shortcomings\u201d on EQIP. Reliability scores did not differ significantly (DISCERN,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.188; EQIP,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.935).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>LLMs show promise in supporting patient education on AMI. While ChatGPT\u20104o offers superior accuracy and reliability, Claude 3.7 enhances accessibility through clearer language. This is the first study comparing three LLMs for AMI education in an emergency context, underscoring that physician oversight remains essential for educational applications in emergency medicine.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/emmi\/5530861","type":"journal-article","created":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T07:16:52Z","timestamp":1781335012000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Accuracy and Reliability of AI Models in Emergency Myocardial Infarction Education"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6874-5277","authenticated-orcid":false,"given":"\u0130brahim","family":"Korkmaz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,13]]},"reference":[{"key":"e_1_2_14_1_2","unstructured":"Cardiovascular Diseases (CVDs) [\u0130nternet] [cited 2025 March 31]. Available from:https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds)."},{"key":"e_1_2_14_2_2","unstructured":"ESC Guidelines for the Management of Acute Coronary Syndromes 2023 https:\/\/www.escardio.org\/Guidelines\/Clinical-Practice-Guidelines\/Acute-Coronary-Syndromes-ACS-Guidelines."},{"key":"e_1_2_14_3_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000001309"},{"key":"e_1_2_14_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipha.2024.03.002"},{"key":"e_1_2_14_5_2","doi-asserted-by":"publisher","DOI":"10.1080\/10872981.2023.2246781"},{"key":"e_1_2_14_6_2","article-title":"Chatbots in Health Care: Connecting Patients to Information: Emerging Health Technologies [Internet]","author":"Clark M.","year":"2024","journal-title":"Ottawa (ON): Canadian Journal of Health Technologies"},{"key":"e_1_2_14_7_2","volume-title":"Inferior STEMI","author":"Burns 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3]https:\/\/readable.com\/readability\/coleman-liau-readability-index\/."},{"key":"e_1_2_14_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.hlc.2024.03.005"},{"key":"e_1_2_14_15_2","doi-asserted-by":"publisher","DOI":"10.5543\/tkda.2024.78131"},{"key":"e_1_2_14_16_2","doi-asserted-by":"publisher","DOI":"10.18203\/2320-6012.ijrms20244094"},{"key":"e_1_2_14_17_2","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.74398"},{"key":"e_1_2_14_18_2","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.69996"},{"key":"e_1_2_14_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11657-024-01376-5"},{"key":"e_1_2_14_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpcardiol.2023.102349"},{"key":"e_1_2_14_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mayocp.2020.01.038"},{"key":"e_1_2_14_22_2","doi-asserted-by":"publisher","DOI":"10.1253\/circj.cj-20-1121"},{"key":"e_1_2_14_23_2","doi-asserted-by":"publisher","DOI":"10.3390\/jpm14060656"},{"key":"e_1_2_14_24_2","doi-asserted-by":"publisher","DOI":"10.1093\/europace\/euad369"},{"key":"e_1_2_14_25_2","doi-asserted-by":"publisher","DOI":"10.1080\/10903127.2024.2376757"},{"key":"e_1_2_14_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11886-025-02217-8"},{"key":"e_1_2_14_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacadv.2024.101314"},{"key":"e_1_2_14_28_2","article-title":"Reclassifying 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