Evaluation of AI Chatbots in Tooth Avulsion Management According to the International Association of Dental Traumatology Guidelines.
Introduction: This study aimed to evaluate the extent to which widely used artificial intelligence (Al)-based chatbots adhere to the 2020 International Association of Dental Traumatology (IADT) guidelines for the management of tooth avulsion and to assess the accuracy of the bibliographic references...
| Publicado en: | Lokman Hekim Health Sciences Vol. 6; no. 2; pp. 255 - 268 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
KARE Publishing
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194639951&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194639951 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 27917835 N1MV jtl: Lokman Hekim Health Sciences issn: 27917835 maglogo: N pubinfo: dt: Jun2026 vid: 6 iid: 2 pid: 62027 pub: KARE Publishing artinfo: ui: 194639951 194639951 194639951 10.14744/lhhs.2026.38881 194639951 ppf: 255 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Evaluation of AI Chatbots in Tooth Avulsion Management According to the International Association of Dental Traumatology Guidelines. aug: au: Özdemir, Merve Manav, Esra Yıldırım affil: Department of Pediatric Dentistry, Faculty of Dentistry, Lokman Hekim University, Ankara, Türkiye sug: subj: Artificial Intelligence Chatbot Tooth Avulsion Therapy Dental Organizations Guideline Adherence Bibliography and References International Agencies Human Cross Sectional Studies Dental Care Quality of Health Care Decision Support Systems, Clinical Emergency Care ab: Introduction: This study aimed to evaluate the extent to which widely used artificial intelligence (Al)-based chatbots adhere to the 2020 International Association of Dental Traumatology (IADT) guidelines for the management of tooth avulsion and to assess the accuracy of the bibliographic references (i.e., complete citation details including title, authors, journal, year, and DOI) they generate. Methods: This cross-sectional observational study assessed four AI-based chatbots (ChatGPT-5.2, Perplexity AI, Gemini 2.5 Flash, and DeepSeek-v3.2) using ten standardized, clinician-directed avulsion scenarios aligned with the 2020 IADT guidelines. Each scenario was submitted once per chatbot, without iterative prompting, on 3 January 2026. Scenarios varied by extra-oral dry time, storage medium, apex maturity, dentition type, and replantation timing. Responses were evaluated using the 9-item IADT Compliance Index. Bibliographic accuracy was assessed using the reference hallucination score (RHS). Results: No statistically significant difference was observed in overall normalized compliance scores among the chatbots (p=0.089). However, significant between-model differences emerged in technically critical domains, including root surface cleaning (p=0.017), and splint type and duration (p<0.001). ChatGPT-5.2 and Perplexity AI consistently outperformed Gemini 2.5 Flash and DeepSeek-v3.2. Although RHS values did not differ significantly between models (p=0.114), all chatbots demonstrated occasional reference hallucinations. Discussion and Conclusion: Performance was higher in simpler scenarios, such as immediate replantation, whereas more complex conditions - particularly prolonged dry time and primary tooth avulsion - showed lower compliance and greater variability. Although chatbots reproduce general principles, limitations restrict reliability; thus, they should be used with clinician supervision. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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