Preliminary Evaluation of Large Language Models in Kennedy Classification and Removable Partial Denture Planning: An In Silico Study

BACKGROUND This study evaluated text-based large language models for Kennedy classification and removable partial denture planning in partially edentulous cases, where comparative evidence on accuracy remains limited. MATERIAL AND METHODS Twenty-six partially edentulous cases, defined according to the Federation Dentaire Internationale tooth numbering system and classified as Kennedy Classes I to IV, were included. Case scenarios were constructed by an experienced prosthodontist. Questions were independently submitted to 4 large language models without prior training or prompting: ChatGPT-5.2, Claude Sonnet 4.6, Gemini 3 Flash, and Perplexity. Responses were evaluated by 2 independent prosthodontists (different from the case developer) - blinded to the identity of each model - using predefined criteria for Kennedy classification accuracy and prosthetic planning consistency. Statistical analyses included intergroup comparisons and effect size estimation. RESULTS Statistically significant differences were identified among the models in both tasks (P<0.001). For Kennedy classification, the effect size was moderate (Kendall's W=0.402). Gemini 3 Flash achieved the highest mean score, followed by ChatGPT-5.2, Perplexity, and Claude Sonnet 4.6. Similarly, significant differences were observed in removable partial denture planning performance (Kendall's W=0.424); Gemini 3 Flash scored significantly higher than the other models (P≤0.001). CONCLUSIONS Large language models showed variable performance under zero-shot conditions. Although Gemini 3 Flash achieved higher scores, the moderate effect sizes warrant cautious interpretation. These models may serve as adjunctive decision-support tools in prosthodontics but cannot replace clinical judgment.