{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T13:21:57Z","timestamp":1787836917324,"version":"build-2784847793"},"reference-count":50,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"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":["Dental Traumatology"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Traumatic dental injuries require rapid, accurate, and context\u2010sensitive decisions, particularly during emergencies such as tooth avulsion, luxation injuries, and trauma involving the primary dentition. Artificial intelligence (AI) chatbots and large language models (LLMs) are increasingly used by patients, caregivers, students, and clinicians for immediate information and decision support, yet their reliability in dental trauma remains uncertain. This systematic review aimed to evaluate the accuracy, consistency, safety, guideline concordance, and clinical applicability of AI chatbots and LLMs for traumatic dental injury management and decision support. PubMed, Scopus, and Embase were searched through April 2026. Eligible studies evaluated AI\u2010generated responses against clinical guidelines, expert assessment, validated dental trauma scenarios, photographs, real cases, standardized questions, or other reproducible reference standards. Twenty\u2010eight studies met the eligibility criteria. A function\u2010based narrative synthesis classified the evidence into five translational functions: patient\u2010 and caregiver\u2010facing emergency guidance, guideline\u2010based diagnostic and management benchmarking, domain\u2010specific or document\u2010grounded workflows, multimodal and real\u2010case validation, and avulsion\u2010specific prognostic or advanced decision support. An exploratory meta\u2010analysis of nine study\u2010level estimates with comparable binary outcomes demonstrated a pooled proportion of accurate or guideline\u2010concordant responses of 81.7%, although heterogeneity was very high. Across studies, performance varied according to model, prompt format, reference standard, input type, and assessment methodology. Safety concerns included incomplete recommendations, misleading information, inconsistent responses, limited readability, unreliable references, and injury\u2010specific performance variability. Overall, current evidence indicates that AI chatbots and LLMs can support dental trauma information retrieval and supervised clinical decision support, particularly when constrained by curated guidelines or domain\u2010specific workflows. However, the available evidence remains limited by substantial methodological heterogeneity and limited real\u2010world validation and therefore does not support the autonomous use of these systems for emergency traumatic dental injury management.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Trial Registration:<\/jats:bold>\n                    PROSPERO; CRD420261394046\n                  <\/jats:p>","DOI":"10.1111\/edt.70122","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T12:58:19Z","timestamp":1787835499000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Chatbots and Large Language Models for Traumatic Dental Injury Management and Decision Support: A Systematic Review With Function\u2010Based Narrative Synthesis and Exploratory Meta\u2010Analysis"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3663-1416","authenticated-orcid":false,"given":"Carlos M.","family":"Ardila","sequence":"first","affiliation":[{"name":"Department of Periodontics, Saveetha Dental College, and Hospitals Saveetha Institute of Medical and Technical Sciences, Saveetha University  Chennai Tamil Nadu India"},{"name":"Basic Sciences Department, Biomedical Stomatology Research Group, Faculty of Dentistry Universidad de Antioquia (U de A)  Medell\u00edn Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2431-7489","authenticated-orcid":false,"given":"Eliana","family":"Pineda\u2010V\u00e9lez","sequence":"additional","affiliation":[{"name":"Basic Sciences Department, Biomedical Stomatology Research Group, Faculty of Dentistry Universidad de Antioquia (U de A)  Medell\u00edn Colombia"},{"name":"Faculty of Dentistry Instituci\u00f3n Universitaria Visi\u00f3n de Las 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