The tissue engineering (TE) of dental oral tissue is facing significant changes in clinical treatments in dentistry. TE is based on a stem cell, signaling molecule, and scaffold triad that must be known and calibrated with attention to specific sectors in dentistry. This review article shows a summary of micro- and nanomorphological characteristics of dental tissues, of stem cells available in the oral region, of signaling molecules usable in TE, and of scaffolds available to guide partial or total reconstruction of hard, soft, periodontal, and bone tissues. Some scaffoldless techniques used in TE are also presented. Then actual and future roles of nanotechnologies about TE in dentistry are presented.

Background: The spread of artificial intelligence (AI) has led to transformative advancements in diverse sectors, including healthcare. Specifically, generative writing systems have shown potential in various applications, but their effectiveness in clinical settings has been barely investigated. In this context, we evaluated the proficiency of ChatGPT-4 in diagnosing gonarthrosis and coxarthrosis and recommending appropriate treatments compared with orthopaedic specialists. Methods: A retrospective review was conducted using anonymized medical records of 100 patients previously diagnosed with either knee or hip arthrosis. ChatGPT-4 was employed to analyse these historical records, formulating both a diagnosis and potential treatment suggestions. Subsequently, a comparative analysis was conducted to assess the concordance between the AI’s conclusions and the original clinical decisions made by the physicians. Results: In diagnostic evaluations, ChatGPT-4 consistently aligned with the conclusions previously drawn by physicians. In terms of treatment recommendations, there was an 83% agreement between the AI and orthopaedic specialists. The therapeutic concordance was verified by the calculation of a Cohen’s Kappa coefficient of 0.580 (p < 0.001). This indicates a moderate-to-good level of agreement. In recommendations pertaining to surgical treatment, the AI demonstrated a sensitivity and specificity of 78% and 80%, respectively. Multivariable logistic regression demonstrated that the variables reduced quality of life (OR 49.97, p < 0.001) and start-up pain (OR 12.54, p = 0.028) have an influence on ChatGPT-4’s recommendation for a surgery. Conclusion: This study emphasises ChatGPT-4’s notable potential in diagnosing conditions such as gonarthrosis and coxarthrosis and in aligning its treatment recommendations with those of orthopaedic specialists. However, it is crucial to acknowledge that AI tools such as ChatGPT-4 are not meant to replace the nuanced expertise and clinical judgment of seasoned orthopaedic surgeons, particularly in complex decision-making scenarios regarding treatment indications. Due to the exploratory nature of the study, further research with larger patient populations and more complex diagnoses is necessary to validate the findings and explore the broader potential of AI in healthcare. Level of Evidence: Level III evidence.

Background: Large Language Models (LLMs) such as ChatGPT are gaining attention for their potential applications in healthcare. This study aimed to evaluate the diagnostic sensitivity of various LLMs in detecting hip or knee osteoarthritis (OA) using only patient-reported data collected via a structured questionnaire, without prior medical consultation. Methods: A prospective observational study was conducted at an orthopaedic outpatient clinic specialized in hip and knee OA treatment. A total of 115 patients completed a paper-based questionnaire covering symptoms, medical history, and demographic information. The diagnostic performance of five different LLMs—including four versions of ChatGPT, two of Gemini, Llama, Gemma 2, and Mistral-Nemo—was analysed. Model-generated diagnoses were compared against those provided by experienced orthopaedic clinicians, which served as the reference standard. Results: GPT-4o achieved the highest diagnostic sensitivity at 92.3 %, significantly outperforming other LLMs. The completeness of patient responses to symptom-related questions was the strongest predictor of accuracy for GPT-4o (p < 0.001). Inter-model agreement was moderate among GPT-4 versions, whereas models such as Llama-3.1 demonstrated notably lower accuracy and concordance. Conclusions: GPT-4o demonstrated high accuracy and consistency in diagnosing OA based solely on patient-reported questionnaires, underscoring its potential as a supplementary diagnostic tool in clinical settings. Nevertheless, the reliance on patient-reported data without direct physician involvement highlights the critical need for medical oversight to ensure diagnostic accuracy. Further research is needed to refine LLM capabilities and expand their utility in broader diagnostic applications.

Objectives: This preliminary study aimed to clinically assess the precision of a novel optical jaw tracking system (JTS) in registering mandibular movements (MMs) of protrusion and mediotrusion. Methods: Twenty healthy participants underwent recordings using Cyclops JTS (Itaka Way Med) for functional MMs of protrusion and laterotrusion by two trained clinicians. Each subject performed five registrations at different times according to a standardized pattern within one-month period. The angulations of protrusive and mediotrusive functional paths within the first 2 mm from the maximal intercuspal position (MIP) were calculated for each trace, using a data software for angle measurements. Descriptive statistics were used to assess the repeatability of the recordings for each participant and MM. Additionally, inferential statistics were carried out on standard deviation values obtained (α=0.05). Results: The overall precision for all the patients was 7.07±3.37° for the protrusion angle, 5.24±2.24° for right laterotrusion and 5.14±3.06° for left laterotrusion angles. The protrusion angle ranged from 3.08° to 13.57°, while the right and left laterotrusion ranged from 1.82° to 9.42° and from 1.58° to 10.59°, respectively. No statistically significant differences were observed between different functional MM types and gender (p > 0.05). Conclusions: Recordings functional MMs of mediotrusion and protrusion using Cyclops JTS showed consistent repeatability, regardless of gender and functional MM type. The results revealed non-negligible variations that may be due to the patients’ abilities to precisely reproduce jaw movements or to the operator's ability to consistently connect the kinesiograph. Clinical significance Capturing functional MMs digitally and importing the data into dental CAD software is essential for virtual waxing in prosthetic rehabilitations to design a functionalized adapted occlusion. Establishing the repeatability of MM recordings by a JTS is a crucial step in better understanding this novel JTS in the market. This process could facilitate the interpretation of cusp angles, aid in CAD dynamic technical modeling, and enhance clinical data communication between clinicians and technicians in a modern workflow.