Objectives: Digitalization has revolutionized dentistry, introducing advanced technological tools that improve diagnostic accuracy and access to healthcare. This study aims to examine the effects of integrating digital technologies into the dental field, analyzing the associated benefits and risks, with particular paid attention to the therapeutic relationship and decision-making autonomy. Materials and Methods: A literature search was conducted in PubMed, Scopus, Web of Science, and Cochrane Library, complemented by Google Scholar for non-indexed studies. The selection criteria included peer-reviewed studies published in English between 2014 and 2024, focusing on digital dentistry, artificial intelligence, and medical ethics. This is a narrative review. Elements of PRISMA guidelines were applied to enhance transparency in reporting. Results: The analysis highlighted that although digital technologies and AI offer significant benefits, such as more accurate diagnoses and personalized treatments, there are associated risks, including the loss of empathy in the dentist–patient relationship, the risk of overdiagnosis, and the possibility of bias in the data. Conclusions: The balance between technological innovation and the centrality of the dentist is crucial. A human and ethical approach to digital medicine is essential to ensure that technologies improve patient care without compromising the therapeutic relationship. To preserve the quality of dental care, it is necessary to integrate digital technologies in a way that supports, rather than replaces, the therapeutic relationship.

The purpose of this study is a new method that can help to identify the sex through the study of palatal rugae, comparing sagittal sections of the hard palate using Cartesian coordinates and evaluating the assistance given by digital technology and its applicability in this method. In this study, 57 digital impressions were examined and divided in two groups based on sex. Results: 2223 impression sections were studied and 145 coordinates that were present with a frequency greater than 50% in one or both groups were obtained: 52 discriminating traits (DT) in the male group, 29 discriminating traits in the female group, and 64 common traits (CT). The DTs in the female group showed no statistically significant difference from the same coordinates in the male one (p = 0.832). Statistically significant differences were observed in the DTs in the male group compared to the same coordinates in the female group (p = 0.018). No statistically significant differences were observed in the frequency of DTs in both sexes (p = 0.056). Further research in forensic odontology is needed to determine its scientific certainty. It is certain that digital technology may one day be a valuable support for the forensic odontologist but to date the lack of dedicated and certified programs limits its reliability.

Antimicrobial resistance (AMR) is widely recognized as a major global public health threat, yet its origins and implications extend beyond the simple misuse or overuse of antibiotics. This study explores AMR as a complex, multifactorial phenomenon shaped by biological, clinical, dental, environmental, and social dynamics, with particular attention to the emerging role of dentistry. A narrative literature review was performed, drawing from textbooks, peer-reviewed articles, and official World Health Organization (WHO) reports, with emphasis on recent findings on periodontal biofilms as reservoirs of resistance genes. The analysis shows that AMR develops through bacterial mutations, horizontal gene transfer, environmental contamination, healthcare-associated practices, and patient behaviors, all of which interact to sustain its spread. Within dentistry, subgingival microresistances are gaining relevance, complicating treatment strategies and underscoring the need for more conscious clinical decision-making. The findings suggest that reducing antibiotic prescriptions or developing new drugs alone will not suffice; instead, a systemic, interdisciplinary approach is required, integrating microbiology, clinical practice, public health, and institutional responsibility. Such awareness is essential to confront the significant clinical, economic, and social implications of AMR and to foster strategies capable of addressing its complex and evolving nature.

Rapid maxillary expansion (RME) is considered an effective treatment in the management of sleep-disordered breathing (SDB) and malocclusions in pediatric patients, not only because it is able to correct the transverse maxillary deficiency, but it also widens the floor of the nasal cavities, leading to a drastic and immediate reduction in air resistance and facilitating a normal nasal breathing pattern. The aim of this study was to evaluate cephalometric changes in the upper airway’s dimensions and facial morphology in pediatric SDB patients treated with RME, comparing data with a no-SDB group treated with RME for malocclusion. In this retrospective study, pre-treatment and post-treatment cephalometric variables were measured on lateral skull radiographs from 20 SDB pediatric patients (nine males and 11 females) aged 6 to 9 years (mean age 7.61 ± 0.6), treated with a rapid maxillary expander, and 20 control patients without SDB (nine males and 11 females) aged 6 to 11 years (mean age 8.4 ± 0.5). In both groups, there were statistically significant changes in the variables indicating the airway’s dimensions and mandibular sagittal position in relation to the cranial base, with a skeletal class II correction in SDB children. Rapid maxillary expansion is associated with an increase in upper-airway dimensions in SDB children, as well as in control healthy subjects, with a possible correction of class II relationship.

Background: Cheiloscopy, a forensic technique based on the uniqueness of labial traces, has traditionally relied on analog methods. While simple, these methods present significant limitations in terms of precision and reproducibility. The introduction of 3D technology, specifically high-resolution optical scanners, represents a pivotal advancement in overcoming these challenges. Objective: This pilot study aimed to explore the feasibility and potential advantages of 3D cheiloscopy in forensic science by analyzing its precision and repeatability compared to traditional analog methods. Methods: Two participants were selected as a pilot sample, and their lip impressions were captured using both analog techniques and a high-resolution intraoral scanner (Medit i700). A total of 40 samples were collected, equally distributed among operators with varying levels of experience. Surface deviation analysis was performed to compare the methods. Results: The 3D models demonstrated greater uniformity and resolution compared to the analog impressions, which were more prone to errors caused by material quality or operator pressure. Surface deviation analysis showed an average similarity of 70%, with a standard deviation of 0.229 mm. The digital methods also significantly reduced operator-dependent variability. Conclusions: This pilot study highlights the potential of 3D cheiloscopy to improve precision and standardization in forensic applications. While the small sample size limits the generalizability of the findings, the results provide a foundation for further research with larger and more diverse datasets. Future studies should explore the capability of 3D cheiloscopy to accurately match individuals and further validate its applicability in forensic and clinical contexts.

Artificial intelligence (AI) is profoundly transforming medicine and healthcare, evolving from analytical tools aimed at automating specific tasks to integrated components of complex socio-technical systems. This work presents a conceptual and theoretical review proposing the Model–Context–Relation (M–C–R) framework to interpret how the effectiveness of Artificial Intelligence (AI) in medicine and healthcare emerges from the dynamic alignment among algorithmic, contextual, and relational dimensions. No new patient-level data were generated or analyzed. Through a qualitative conceptual framework analysis, the study integrates theoretical, regulatory, and applicative perspectives, drawing on the Revision of the Semiological Paradigm developed by the Palermo School, as well as on major international guidelines (WHO, European AI Act, FDA). The results indicate that AI-supported processes have been reported in the literature to improve clinical accuracy and workflow efficiency when appropriately integrated, yet its value depends on contextual adaptation and human supervision rather than on algorithmic performance alone. When properly integrated, AI functions as a digital semiotic extension of clinical reasoning and may enhance the physician’s interpretative capacity without replacing it. The M–C–R framework enables understanding of how performance, ethical reliability, and organizational sustainability emerge from the alignment between the technical model, the context of use, and relational trust. In this perspective, AI is conceptualized not as a decision-maker but as an adaptive cognitive partner, fostering a reflective, transparent, and person-centered medicine. The proposed approach supports the design of sustainable and ethically responsible AI systems within a Medicine of Complexity, in which human and artificial intelligence co-evolve to strengthen knowledge, accountability, and equity in healthcare systems.