{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:04:35Z","timestamp":1791911075895,"version":"4.3.4"},"reference-count":33,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"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":["Journal of Dental Education"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Temporomandibular disorders (TMDs) and orofacial pain (OFP) demand advanced diagnostic and clinical reasoning skills in dental education. Traditional simulations with real patients face limitations in availability and standardization. Generative artificial intelligence (GAI), such as ChatGPT\u20103.5, has emerged as a potential tool for clinical training.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This blinded, cross\u2010sectional, crossover study involved 30 undergraduate dental students, each completing two simulated cases: one with ChatGPT\u20103.5 and one with standardized real patients. Cases were developed and validated by TMD\/OFP specialists via the Delphi method and incorporated into the AI through structured prompts. Quantitative parameters (such as responses, word count, follow\u2010up questions, reformulations, and diagnostic accuracy) and qualitative aspects (such as empathy, clarity, and relevant information) were analyzed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      GAI simulations provided higher information density (231\u00a0vs.167 relevant units;\n                      <jats:italic>p\u00a0<\/jats:italic>\n                      &lt;\u00a00.001) and clearer reasoning flow. Students interacting with real patients asked more follow\u2010up questions (\n                      <jats:italic>p\u00a0<\/jats:italic>\n                      =\u00a00.004) and required more reformulations (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.011), indicating more adaptive communication. Diagnostic accuracy did not differ significantly (\n                      <jats:italic>p\u00a0<\/jats:italic>\n                      &gt;\u00a00.05). Relevant information correlated positively with diagnostic accuracy (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.484;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.007), whereas total word count correlated negatively (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a0\u22120.386;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.035).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ChatGPT\u20103.5 matched real patient simulations in diagnostic reasoning for TMD\/OFP. Combining GAI's scalability and standardization with real patient variability may optimize clinical competency training.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jdd.70104","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T06:07:49Z","timestamp":1763014069000},"page":"1259-1271","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Generative Artificial Intelligence\u2013Driven Clinical Case Simulation in Temporomandibular Disorder Education: ChatGPT Versus Real Patients"],"prefix":"10.1002","volume":"90","author":[{"given":"Paula","family":"Rodrigues\u2010Pereira","sequence":"first","affiliation":[{"name":"Departamento de Odontologia Restauradora Faculdade de Odontologia de Ribeir\u00e3o Preto Universidade de S\u00e3o Paulo (FORP\/USP)  Ribeir\u00e3o Preto S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Am\u00e1lia Pereira","family":"Dias\u2010Cal\u00e7as","sequence":"additional","affiliation":[{"name":"Departamento de Odontologia Restauradora Faculdade de Odontologia de Ribeir\u00e3o Preto Universidade de S\u00e3o Paulo (FORP\/USP)  Ribeir\u00e3o Preto S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Moreira M\u00e9lo","sequence":"additional","affiliation":[{"name":"Departamento de Odontologia Restauradora Faculdade de Odontologia de Ribeir\u00e3o Preto Universidade de S\u00e3o Paulo (FORP\/USP)  Ribeir\u00e3o Preto S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa de Oliveira","family":"Melchior","sequence":"additional","affiliation":[{"name":"Departamento de Odontologia Restauradora Faculdade de Odontologia de Ribeir\u00e3o Preto Universidade de S\u00e3o Paulo (FORP\/USP)  Ribeir\u00e3o Preto S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas","family":"Gaspar Ribeiro","sequence":"additional","affiliation":[{"name":"Departamento de Cl\u00ednica M\u00e9dica Faculdade de Medicina de Ribeir\u00e3o Preto Universidade de S\u00e3o Paulo (FMRP\/USP)  Ribeir\u00e3o Preto S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ant\u00f4nio","family":"Pazin\u2010Filho","sequence":"additional","affiliation":[{"name":"Departamento de Cl\u00ednica M\u00e9dica Faculdade de Medicina de Ribeir\u00e3o Preto Universidade de S\u00e3o Paulo (FMRP\/USP)  Ribeir\u00e3o Preto S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jardel Francisco","family":"Mazzi\u2010Chaves","sequence":"additional","affiliation":[{"name":"Departamento de Odontologia Restauradora Faculdade de Odontologia de Ribeir\u00e3o Preto Universidade de S\u00e3o Paulo (FORP\/USP)  Ribeir\u00e3o Preto S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"La\u00eds Valencise","family":"Magri","sequence":"additional","affiliation":[{"name":"Departamento de Odontologia Restauradora Faculdade de Odontologia de Ribeir\u00e3o Preto Universidade de S\u00e3o Paulo (FORP\/USP)  Ribeir\u00e3o Preto S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpain.2013.09.010"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/0022034516653743"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/adj.12785"},{"issue":"6","key":"e_1_2_10_5_1","first-page":"378","article-title":"Diagnosis and Treatment of Temporomandibular Disorders","volume":"15","author":"Gauer R. 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