Diagnostic accuracy of ChatGPT-5 in evaluating root canal treatment and periapical pathosis on periapical radiographs.

Abstract

Objectives: Artificial intelligence (AI) chatbots such as ChatGPT-5 (OpenAI) are increasingly used for dental radiograph interpretation, especially among patients seeking self-assessment. However, their diagnostic accuracy in endodontics remains unclear.

Methods: This cross-sectional STARD-AI-compliant study analyzed 271 anonymized periapical radiographs of endodontically treated posterior teeth, classified as straightforward (n = 167) or complex (n = 104), using standardized ChatGPT-5 prompts. Diagnostic criteria included obturation length (short, adequate, long), presence of voids, and periapical pathosis. Results were compared to those of a panel of general dentists and a reference standard from endodontic specialists. Sensitivity, specificity, and accuracy were calculated using the McNemar test (p < 0.05).

Results: ChatGPT-5 demonstrated high specificity (up to 99.3%) for normal or adequately treated findings but low sensitivity for short (13.7%) or long (0.0%) obturations, voids (9.0%-22.7%), and periapical lesions (10.5%-28.6%). Overall accuracy (54.0%-63.2%) was significantly lower than that of general dentists (76.0%-85.6%) (p < 0.001).

Conclusions: Although ChatGPT-5 achieved high specificity, its low sensitivity and overall accuracy limit diagnostic reliability. Expert clinician oversight remains essential for accurate interpretation and treatment planning.