The widespread implementation of artificial intelligence technologies provides an appealing alternative to traditional search engines for online patient healthcare education. This study assessed ChatGPT-3.5’s capabilities as a source of obstructive sleep apnea (OSA) information, using Google Search as a comparison. Ten frequently searched questions related to OSA were entered into Google Search and ChatGPT-3.5. The responses were assessed by two independent researchers using the Global Quality Score (GQS), Patient Education Materials Assessment Tool (PEMAT), DISCERN instrument, CLEAR tool, and readability scores (Flesch Reading Ease and Flesch–Kincaid Grade Level). ChatGPT-3.5 significantly outperformed Google Search in terms of GQS (5.00 vs. 2.50, p < 0.0001), DISCERN reliability (35.00 vs. 29.50, p = 0.001), and quality (11.50 vs. 7.00, p = 0.02). The CLEAR tool scores indicated that ChatGPT-3.5 provided excellent content (25.00 vs. 15.50, p < 0.001). PEMAT scores showed higher understandability (60–91% vs. 44–80%) and actionability for ChatGPT-3.5 (0–40% vs. 0%). Readability analysis revealed that Google Search responses were easier to read (FRE: 56.05 vs. 22.00; FKGL: 9.00 vs. 14.00, p < 0.0001). ChatGPT-3.5 delivers higher quality and more comprehensive OSA information compared to Google Search, although its responses are less readable. This suggests that while ChatGPT-3.5 can be a valuable tool for patient education, efforts to improve readability are necessary to ensure accessibility and utility for all patients. Healthcare providers should be aware of the strengths and weaknesses of various healthcare information resources and emphasize the importance of critically evaluating online health information, advising patients on its reliability and relevance.

Background/Objectives: Accurate identification of deciduous and permanent teeth on panoramic radiographs (PRs) during mixed dentition is fundamental for early detection of eruption disturbances, yet relies heavily on clinician experience due to developmental variability. This study aimed to develop a deep learning model for automated tooth detection and segmentation in pediatric PRs during mixed dentition. Methods: A retrospective dataset of 250 panoramic radiographs from patients aged 6–13 years was analyzed. A customized YOLOv11-based model was developed using a novel hybrid pre-annotation strategy leveraging transfer learning from 650 publicly available adult radiographs, followed by expert manual refinement. Performance evaluation utilized mean average precision (mAP), F1-score, precision, and recall metrics. Results: The model demonstrated robust performance with mAP_0.5 = 0.963 [95%CI: 0.944–0.983] and macro-averaged F1-score = 0.953 [95%CI: 0.922–0.965] for detection. Segmentation achieved mAP_0.5 = 0.890 [95%CI: 0.857–0.923]. Stratified analysis revealed excellent performance for permanent teeth (F1 = 0.977) and clinically acceptable accuracy for deciduous teeth (F1 = 0.884). Conclusions: The automated system achieved near-expert accuracy in detecting and segmenting teeth during mixed dentition using an innovative transfer learning approach. This framework establishes reliable infrastructure for AI-assisted diagnostic applications targeting eruption or developmental anomalies, potentially facilitating earlier detection while reducing clinician-dependent variability in mixed dentition evaluation.

Disturbed nocturnal sleep contributes to the central sensitization of pain, thus predisposing to orofacial pain. Central disorders of hypersomnolence are characterized by excessive daytime sleepiness (EDS) not linked to impairment of nocturnal sleep or misaligned circadian rhythms. The main disorder of this group is narcolepsy type 1 (NT1), which seems to be related to alterations in pain perception mediation, supposedly caused by low orexin levels. The aim of this study was to evaluate the pain sensitivity and the prevalence of temporomandibular disorders (TMDs) in patients with NT1. After a 3-day hospital evaluation with laboratory polysomnography, 39 consecutive adult patients diagnosed with NT1 and 39 matched heathy controls were evaluated by means of Axis I and Axis II of the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) protocol. Furthermore, pain sensitivity was investigated by measuring the pressure pain thresholds (PPTs) on the head–neck muscles by means of a Fischer algometer. No significant differences were found between the PPTs of the two groups for all the muscles evaluated, nor in the prevalence of TMD diagnoses, but the NT1 group reported significantly higher values in the Patient Health Questionnaire (PHQ-9), corresponding to a depressive state. The present study presents an important investigation into NT1 patients, showing no alterations in pain perception and no differences in the prevalence of TMD diagnosis compared to the controls.

As the use of artificial intelligence (AI) chatbots for medical queries expands, their reliability may vary as models evolve. We longitudinally assessed the quality, reliability, and readability of information on temporomandibular disorders (TMD) generated by three widely used chatbots (ChatGPT, Gemini, and Microsoft Copilot). Ten TMD questions were submitted to each chatbot at two timepoints (T1: February 2024; T2: February 2025). Two blinded evaluators independently assessed all answers using validated tools like the Global Quality Score (GQS), PEMAT, DISCERN, CLEAR, Flesch Reading Ease (FRE), and Flesch–Kincaid Grade Level (FKGL) tools. Analyses followed METRICS guidance. Comparisons between models and across timepoints were conducted using non-parametric tests. At T1, Copilot scored significantly lower in GQS, CLEAR appropriateness, and relevance (p < 0.01), while ChatGPT provided less evidence-based content than its counterparts (p < 0.001). Reliability was poor across models (mean DISCERN score: 34.73 ± 9.49), and readability was difficult (mean FRE: 34.64; FKGL: 14.13). At T2, performances improved across chatbots, particularly for Copilot, yet actionability remained limited and citations were inconsistent. This year-long longitudinal analysis shows an overall improvement in chatbot performance, although concerns regarding information reliability persist. These findings underscore the importance of human oversight of AI-mediated patient information, reaffirming that clinicians should remain the primary source of patient education.