Clinical decision support of advanced large language models in endodontic disease

Large language models (LLMs) exhibit significant potential for clinical decision support, yet their application in endodontic disease remains underexplored.

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.

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.

LLMs hold promise as endodontic decision support tools, though domain-specific refinement is essential for effective clinical application.