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

Vollständiger Abstract

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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.