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.

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/ PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%–75.9%) and no higher than 98.19 (95% CI, 97.8%–98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%–93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.