Paul Michael Howell Dummer

Objective: To evaluate the level of agreement between ChatGPT‐5.5 and human assessments when appraising the methodological quality and overall confidence of the results of systematic reviews (SRs) with meta‐analysis (MA) in endodontology. Methods: SRs with MA related to endodontology and published in English between 2021 and 2024 were included. The methodological quality of the SRs was appraised by three human experts using AMSTAR 2, and a consensus dataset was generated. ChatGPT‐5.5 was applied to generate AMSTAR 2 item‐level ratings and overall confidence classifications for each SR through three newly created accounts. Agreement between consolidated ChatGPT‐5.5 ratings and human consensus was assessed for both overall confidence and each AMSTAR 2 item. Overall‐confidence agreement was evaluated under the primary criterion (Partial Yes = Yes) and an alternative sensitivity criterion (Partial Yes = No). Direct agreement and linearly weighted Cohen's kappa were calculated, and complete 3/3 agreement was used to assess consistency across ChatGPT‐5.5 accounts. Results: One hundred SRs were included. Human consensus and ChatGPT‐5.5 classified 47 and 85 SRs, respectively, as critically low. Under the primary criterion (Partial Yes = Yes), overall confidence matched in 52/100 SRs (52%; weighted κ = 0.182). Under the alternative criterion (Partial Yes = No), agreement increased to 83%, while weighted κ remained similar (κ = 0.173), indicating that direct agreement was sensitive to the handling of Partial Yes. Item‐level agreement varied substantially. Overall confidence was identical across the three ChatGPT‐5.5 accounts in 85 SRs, and global item‐level 3/3 agreement was 86.12%. In the 100 sampled SRs, 15% met the criterion for reporting primary‐study funding and 21% for justifying study designs. Conclusion: Overall‐confidence direct agreement was sensitive to the handling of Partial Yes ratings, whereas chance‐corrected agreement remained similar under both criteria. ChatGPT‐5.5 assigned lower overall confidence classifications more frequently than human consensus, and agreement varied across AMSTAR 2 domains. Although outputs were frequently consistent across the three ChatGPT‐5.5 accounts, between‐account consistency did not ensure concordance with human judgements. The findings do not support the use of ChatGPT‐5.5 as an independent AMSTAR 2 assessor; any exploratory application requires item‐by‐item verification by reviewers with methodological expertise.

Artificial intelligence (AI) is increasingly influencing dental diagnosis and clinical decision‐making, yet many AI systems continue to represent disease and treatment outcomes through discrete categories. Although binary classification has contributed substantially to diagnostic performance, it does not always reflect how clinicians reason in practice. Dental decisions are frequently made under incomplete information, biological variability, and competing clinical findings, requiring dentists to weigh probabilities, alternative explanations, and degrees of confidence rather than simply choosing between two outcomes. This short communication argues that future dental AI should better represent this inherent clinical uncertainty. Fuzzy logic provides one possible conceptual framework, allowing partial membership and graded states rather than forcing complex biological conditions into rigid categories. Its potential value in dentistry extends beyond computation: it may help AI communicate uncertainty in ways that are more consistent with clinical reasoning. Such systems could provide graded diagnostic confidence, competing possibilities, or degrees of treatment suitability, rather than presenting apparently definitive answers. This approach may also support explainability and reduce inappropriate reliance on algorithmic recommendations. The future of dental AI may therefore depend not only on improving predictive accuracy, but also on representing the grey zones in which much of clinical dentistry takes place.

Aim: To develop and validate a machine learning‐based automated computational framework using periapical radiographs for the detection, segmentation and classification of root canal curvature in mandibular premolars with single canals, and to evaluate the contribution of geometric and deep learning features towards diagnostic performance. Methodology: A retrospective cross‐sectional study was performed using 1644 periapical images of mandibular premolars. A four‐stage multi‐model framework was proposed using a single fixed 10‐fold cross‐validation partition applied consistently across all four stages to prevent data leakage between training and evaluation. The object detection models were YOLOv8, YOLOv11 and RF‐DETR. For segmentation of the root canal, YOLOv8‐seg, YOLOv11‐seg and Attention U‐Net were used. The multiparametric feature extraction included five geometric morphometric features automatically derived from a binary mask and 2048 deep convolutional neural network features from ResNet‐50. Classification of curvature was defined using an eXtreme Gradient Boosting (XGBoost) classifier with stratified 10‐fold cross‐validation, with Synthetic Minority Over‐sampling Technique (SMOTE) and algorithmic class weighting applied within the training folds of each cross‐validation iteration. Results: The dataset included 633 straight, 885 moderately curved and 126 severely curved canals. RF‐DETR achieved a recall of 97.5%, precision of 93.3% and F1‐score of 95.4% for premolar detection. The YOLOv11‐seg model was successful in segmenting the root canal with a Dice score of 85.8% and an IoU of 77.5%. All five geometric parameters were statistically significant for different severity groups ( p ‐value < 0.001). For the end‐to‐end curvature classification, the hybrid feature fusion model produced the highest overall metrics, achieving an accuracy of 89.60%, a recall of 83.80% and an F1‐score of 86.77%, compared to the models using either geometric features (88.38% accuracy) or deep learning features alone (82.42% accuracy). Conclusions: The hybrid feature‐fusion model reached the highest overall classification accuracy at 89.60%. Although the geometric‐only model had higher sensitivity for severe curvature with 80.6% compared to 69.1%, Lüiten's angle was the most prominent predictor of curvature severity. Due to the limitations of two‐dimensional radiographs in displaying buccolingual curvatures, this framework works best as a screening aid as it helps identify canals that need adjunctive three‐dimensional evaluation.

This case report describes the conservative management of an incompletely erupted, subgingivally positioned mandibular right second molar with extensive caries and periapical pathosis in close anatomical relationship to the IAN canal. Complete extraction was initially considered as a provisional treatment option; however, CBCT revealed partial encirclement of the IAN canal by the roots, leading to a conservative treatment plan comprising root canal treatment followed by coronectomy. The initial treatment involved the extraction of tooth 48, followed by root canal treatment and tooth restoration of tooth 47; the patient declined coronectomy. At the 1‐year follow‐up, the patient was asymptomatic and periapical radiographs and CBCT scans indicated substantial periapical healing and preservation of the IAN. This case illustrates the role of CBCT‐guided conservative endodontic management of impacted teeth as a viable alternative to extraction in high‐risk anatomical situations.