Purpose: The aim of this study was to evaluate the diagnostic and classification performance of a deep learning (DL) model for peri-implantitis–related bone defects using panoramic radiographs, focusing on defect morphology and severity. Methods: A dataset comprising 1,075 panoramic radiographs from 426 patients with periimplantitis was analyzed. A total of 2,250 implant sites were annotated and categorized based on defect morphology (intraosseous [class I], supracrestal/horizontal [class II], or combined [class III]) and severity (slight, moderate, or severe). The ensemble-based YOLOv8 DL model was trained on 80% of the dataset, with the remaining 20% reserved for testing. Performance was assessed using classification metrics, including accuracy, precision, recall, and F1 score.The diagnostic accuracy of the DL model was also compared with that of 2 board-certified periodontal surgeons. Results: The DL model achieved an overall accuracy of 85.33%, significantly outperforming the periodontal surgeons, who exhibited a mean accuracy of 75.6%. The DL model performed especially well for slight class II defects, with precision and recall values of 100% and 98%, respectively. In contrast, the periodontal surgeons demonstrated higher accuracy in severe cases, particularly for class II defects. Conclusions DL enables reliable and accurate detection of peri-implantitis bone defects.It outperformed periodontal surgeons in overall accuracy, demonstrating its potential as a valuable second-opinion tool to support clinical decision-making. Future research should focus on expanding datasets and incorporating multimodal imaging.

Purpose: This study aimed to evaluate and compare the responses of an artificial intelligence (AI)–powered chatbot and professional periodontists to patient queries in periodontology and implantology, using the Korean Academy of Periodontology’s (KAP) online question and answer (Q&A) section. Methods: In this comparative cross-sectional study, we analyzed 219 patient-submitted periodontal and implant knowledge questions from the KAP online Q&A section. A panel of 10 evaluators—5 periodontists and 5 laypersons—rated both the periodontist’s and the AI chatbot’s responses using standardized scales. We applied the t-test and Spearman correlation coefficients to compare response quality, empathy, consistency, and evaluator preferences. Results: Ten evaluators judged the AI chatbot’s responses to be significantly superior in quality and empathy compared to periodontist replies. A higher proportion of periodontist responses fell below acceptable quality (“very poor” or “poor”) than chatbot responses (28.7% vs. 15.0%; P<0.001), and more chatbot replies were rated “empathetic” or “very empathetic” (62.5% vs. 42.8%; P<0.001). Overall response consistency was deemed satisfactory at 64.2%, with no significant difference in consistency or preference between periodontist and lay evaluators. Conclusions AI-powered chatbots can deliver more accurate and empathetic answers than human periodontists, suggesting their potential role as consultation assistants merits further investigation. The high intraclass correlation coefficient values (0.79–0.93) indicate a high level of agreement among evaluators in both the periodontist and lay evaluator groups, thus confirming the reliability and robustness of the study’s assessment methodology.