Abstract
BACKGROUND: Periodontitis is a chronic gum disease affecting approximately 42% of adults aged 30 and older in the United States. Training dental students to accurately diagnose and manage periodontitis is a critical component of dental education and clinical care. Recent advances in large language models (LLMs) offer new opportunities to support both domains, yet their performance in periodontal diagnosis remains largely unexplored-particularly for newer models such as Generative Pre-trained Transformer 5 (GPT-5). OBJECTIVE: This study conducted an exploratory evaluation of GPT-5's ability to stage and grade periodontitis. METHODS: Twenty-five publicly available clinical cases were identified through Google and PubMed searches. Each case description was entered into GPT-5 using a zero-shot prompting approach, and the model's predictions were compared with the published reference diagnoses. Performance was measured using accuracy and Cohen's kappa. RESULTS: Across these cases, GPT-5 showed marked class-dependent performance and a tendency to overestimate disease severity. Compared with prior models, it achieved comparable or improved performance, with accuracies of 68.0% for staging and 77.3% for grading and corresponding Cohen's kappa values of 0.432 and 0.179, respectively. While staging performance showed fair agreement beyond chance, the low kappa for grading indicates poor agreement and limited reliability in distinguishing periodontal disease severity. CONCLUSIONS: These findings suggest that although GPT-5 shows improvement over previous models, its current diagnostic performance, particularly for periodontitis grading, limits its utility in clinical assessment and educational training. Meaningful application in periodontal diagnosis and training will require substantial improvements in reliability and rigorous validation. The limitations of the study and implications for future development are also discussed.