Introduction: The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long‐term goal of harnessing the power of AI to augment didactic teaching, the objective of this study was to quantify and compare the accuracy of responses provided by ChatGPT (GPT‐4 and GPT‐3.5) and Google Gemini, the three primary large language models (LLMs), to human graduate students (control group) to the annual in‐service examination questions posed by the American Academy of Periodontology (AAP).Methods: Under a comparative cross‐sectional study design, a corpus of 1312 questions from the annual in‐service examination of AAP administered between 2020 and 2023 were presented to the LLMs. Their responses were analyzed using chi‐square tests, and the performance was juxtaposed to the scores of periodontal residents from corresponding years, as the human control group. Additionally, two sub‐analyses were performed: one on the performance of the LLMs on each section of the exam; and in answering the most difficult questions.Results: ChatGPT‐4 (total average: 79.57%) outperformed all human control groups as well as GPT‐3.5 and Google Gemini in all exam years (p < .001). This chatbot showed an accuracy range between 78.80% and 80.98% across the various exam years. Gemini consistently recorded superior performance with scores of 70.65% (p = .01), 73.29% (p = .02), 75.73% (p < .01), and 72.18% (p = .0008) for the exams from 2020 to 2023 compared to ChatGPT‐3.5, which achieved 62.5%, 68.24%, 69.83%, and 59.27% respectively. Google Gemini (72.86%) surpassed the average scores achieved by first‐ (63.48% ± 31.67) and second‐year residents (66.25% ± 31.61) when all exam years combined. However, it could not surpass that of third‐year residents (69.06% ± 30.45).Conclusions: Within the confines of this analysis, ChatGPT‐4 exhibited a robust capability in answering AAP in‐service exam questions in terms of accuracy and reliability while Gemini and ChatGPT‐3.5 showed a weaker performance. These findings underscore the potential of deploying LLMs as an educational tool in periodontics and oral implantology domains. However, the current limitations of these models such as inability to effectively process image‐based inquiries, the propensity for generating inconsistent responses to the same prompts, and achieving high (80% by GPT‐4) but not absolute accuracy rates should be considered. An objective comparison of their capability versus their capacity is required to further develop this field of study.

Taxonomy and classification of a disease contributes to facilitating the diagnosis and treatment planning process and simplifies communication between clinicians. The aim of this study was to provide a critical appraisal based on a systematic review of the single‐rooted extraction socket (ES) classifications and subsequently, introduce a new classification system combining the cornerstones of the previously proposed systems and based on the latest consensus in implant dentistry. Following the systematic search process in PubMed, EMBASE, and SCOPUS databases 13 ES classifications were detected. The most repeated hard and soft tissue factors in the previous classifications were buccal bone dehiscence, interproximal bone, gingival recession, and soft tissue phenotype. However, there was minimal attention to patient‐related factors such as systemic conditions and smoking. Therefore, a new classification system based on the combination of patient‐related factors, clinical and radiographical parameters was proposed. This divides an ES into three types. Class I and II sockets are candidates for receiving immediate implant placement and conversely, a class III socket includes a compromised condition that requires multiple‐stage reconstruction mostly suitable for standard delayed implant placement with alveolar ridge preservation. Within the limitations of this study, the new classification system not only provides comprehensive inclusion of various crucial parameters in implant placement (such as prediction of future implant position and osteotomy difficulty, etc.) but also, in contrast to the previously introduced systems, is able to classify the ES prior to extraction and also, takes into the account the patient‐related factors as the class modifiers following the extraction.

Aim: To assess the reliability of ultrasonographic, cone beam computed tomographic (CBCT), probe transparency and transgingival probing (TGP) methods in evaluating gingival thickness (GT), compared with the gold standard histological assessment. Methods: Sixteen fresh cadaver heads with intact gingivae were used. The sequence for GT measurement included CBCT, ultrasonography, probe transparency, TGP and histology. Both stainless steel periodontal probe and colour‐coded probes were used for transparency. TGP involved a calibrated endodontic spreader, and histologic samples served as a comparative standard. Primary outcomes evaluated accuracy in GT measurement, while secondary outcomes assessed agreement among methods and established an optimal threshold for thin versus thick gingiva. Results: One hundred and fifteen teeth were examined, yielding a mean GT of 1.34 mm histologically. US and CBCT underestimated GT (means of 1.25 mm and 1.13 mm, respectively), while TGP overestimated (1.51 mm). Correlations ( r = 0.88–0.98) and ICC values (0.73–0.95) indicated strong inter‐method agreement. Regression models significantly estimated histological GT from US, CBCT and TGP. A new 1.18 mm cut‐off, based on histology, improved diagnostic accuracy over the traditional 1 mm threshold. Conclusions: While histology remains the GT reference standard, US, CBCT and TGP achieved clinically acceptable accuracy. US showed the highest agreement with histology, followed by TGP and CBCT. The study supports US as the most practical non‐invasive tool, although CBCT and TGP remain viable options. Further clinical validation is recommended, acknowledging the limitations of cadaveric models in reflecting in vivo conditions.