DOI
https://doi.org/10.1016/j.jds.2025.06.007
First Page
191
Last Page
197
Abstract
Background/purpose Large language models (LLMs) exhibit significant potential for clinical decision support, yet their application in endodontic disease remains underexplored. Materials and methods This study assessed the decision-making capabilities of three advanced LLMs (GPT-4o, Claude 3.5, and Grok2) in specialized endodontic contexts. A question bank of 421 multiple-choice questions was constructed across 27 core endodontic topics, including theory, procedures, and 35 complex cases. The three LLMs were tested using standardized prompts, with performance evaluated via topic-stratified accuracy analysis. Results Claude 3.5 achieved the highest overall accuracy (73.39 %), followed by Grok2 (66.27 %) and GPT-4o (46.32 %). Grok2 excelled in complex case analysis (69.57 %). The models performed strongly in theoretical domains (e.g., clinical examination, structural function, pharmacology) but showed limitations in complex scenarios and procedural techniques. Conclusion LLMs hold promise as endodontic decision support tools, though domain-specific refinement is essential for effective clinical application.
Recommended Citation
Li, Jiahe; He, Xian; Wang, Yong; Liu, Yanan; Liu, Jie; Liu, Mingxiao; Huang, Tianyu; and Huang, Zeyu
(2026)
"Clinical decision support of advanced large language models in endodontic disease,"
Journal of Dental Sciences: Vol. 21:
Iss.
1, Article 24.
DOI: https://doi.org/10.1016/j.jds.2025.06.007
Available at:
https://jds.ads.org.tw/journal/vol21/iss1/24
Publication Date
1-1-2026
