Search Results (700)

Background: Sustainable head and neck cancer (HNC) surgery is challenged by environmental impact, workforce shortages, inequitable access to advanced techniques, and policy constraints. Addressing these areas is critical for equitable, high-quality care. Methods: This structured narrative review synthesizes evidence on environmental sustainability, workforce development, technological innovation, health policy, and socioeconomic determinants in HNC surgery, without aiming to provide a systematic or exhaustive evidence synthesis. Sources included peer-reviewed literature, global workforce surveys, and international policy reports, with a focus on disparities between high-income countries (HICs) and low- and middle-income countries (LMICs). Results: Operating rooms produce up to 70% of hospital solid waste and consume 3–6 times more energy than other units; reusable instruments and improved waste segregation can reduce carbon footprints by over 50%. Workforce shortages are severe in LMICs, where subspecialty training is scarce; global partnerships, bidirectional education, and simulation-based learning can expand local capacity. Telemedicine, artificial intelligence, and three-dimensional printing enhance surgical planning, training, and access but may widen disparities without equitable deployment. Policy tools—including diagnosis-related groups, bundled payments, and universal coverage—affect access and innovation uptake. Pandemic preparedness underscores the value of resilient systems with flexible staffing and telehealth integration. Conclusions: HNC surgery requires coordinated action across environmental, workforce, technological, socioeconomic, and policy domains; however, future systematic reviews are needed to comprehensively map the evidence base and assess its methodological quality. Embedding sustainability in clinical practice, ensuring equitable innovation access, and aligning reimbursement with high-value care can strengthen system resilience, improve outcomes, and support long-term surgical service viability. Full article

Objectives: We aimed to descriptively evaluate the feasibility and clinical utility of TowardPi BO (4K ultra-HD microscope integrated with a 400 kHz swept-source intraoperative optical coherence tomography (SS-iOCT) system) in managing various ophthalmic surgical conditions in a real-world setting. Methods: We analyzed surgical videos and data from 123 consecutive cases that underwent elective surgery with the assistance of this SS-iOCT system at Beijing Tongren Hospital between 2 September 2025 and 10 February 2026. Cases were included when the iOCT provided critical, real-time information that directly influenced surgical decision-making or technique modification. Cases were excluded if iOCT served only routine confirmatory or educational purposes without altering the surgical plan. Results: A total of 72 surgical cases were included, comprising 7 intraocular lens implantations with ciliary sulcus fixation, 19 macular holes, 3 cases of macular hole retinal detachment (MHRD), 4 cases of macular schisis with or without foveal detachment (MSRD), 12 cases of submacular hemorrhage, 20 cases of rhegmatogenous retinal detachment (RRD), and 7 intraocular mass lesions. The 400 kHz SS-iOCT significantly aided in surgical visualization, guided real-time decision-making, and prompted modifications in surgical techniques. Conclusions: To our knowledge, this is the first real-world study to evaluate the application of a 400 kHz SS-iOCT system across a wide spectrum of ophthalmic conditions, including its novel use in intraocular tumors. From routine to complex surgical cases, SS-iOCT enhances surgical precision and facilitates real-time decision-making, ultimately contributing to improved surgical outcomes. Full article

Generative Artificial Intelligence (GenAI) in cardiac surgery refers to the integration of advanced computational models, such as Large Language Models (LLMs), to automate and enhance clinical decision-making, preoperative risk assessment, and surgical education. In the context of surgical training, it functions as a personalized pedagogical tool that supports various learning activities, ranging from information acquisition and clinical inquiry to procedural practice, while requiring rigorous human oversight to ensure patient safety and clinical accuracy. (1) Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into health professions education, offering new opportunities for learning; however, its specific application and pedagogical mapping in high-stakes fields such as cardiac surgery remain underexplored. This systematic review investigates how GenAI is utilized in cardiac surgery and surgical education, aligning these uses with Laurillard’s six learning types. (2) Methods: Following the PRISMA 2020 guidelines, we searched the Web of Science Core Collection for studies on GenAI in cardiac surgery, resulting in 42 studies that met the inclusion criteria. Study quality was appraised using the Medical Education Research Study Quality Instrument (MERSQI). (3) Results: GenAI applications most frequently supported clinical inquiry (93.8%) and practice (68.8%), demonstrating expanding efficiency across commercial and open-source models (including ChatGPT-4o, Gemini AI, and emerging reasoning architectures such as DeepSeek) for knowledge acquisition and medical production. While it significantly improves individualized learning and preoperative assessment workflows, its practical role in Discussion and Collaboration remains heavily underutilized, highlighting a distinct shift toward individualized solo professional workflows. (4) Conclusions: GenAI provides a transformative and scalable approach to cardiac surgical training by offering personalized and accessible knowledge retrieval. However, clinical educators and governance bodies must deliberately balance these immediate productivity benefits with long-term concerns regarding structural “hallucinations,” data verifiability, and the preservation of collaborative competencies within modern multidisciplinary Heart Teams. Full article

Obesity poses a significant challenge to societies and healthcare systems worldwide. Metabolic bariatric surgery (MBS) has emerged as a proven primary intervention for severe obesity. This cross-sectional study investigated the interplay between sociodemographic and psychological factors and their influence on attitudes toward eating and body mass index (BMI) among candidates undergoing MBS. Recruitment took place over a period of more than five years. Sociodemographic and BMI data were collected, and psychological evaluations were conducted using standardized self-report questionnaires and clinical interviews. Data were analysed using Pearson and Spearman correlations, multiple linear regression, and bootstrapped mediation analysis. After exclusion of those who did not meet surgical suitability criteria, declined participation, or had incomplete data, the study included 515 participants. Significant associations were found between lower educational attainment, unmarried status, and higher BMI. High neuroticism was linked to increased BMI, depression, and anxiety. Resilience was moderately negatively correlated with anxiety and depressive symptoms and showed a small but statistically significant indirect (partial mediating) effect on the relationship between neuroticism and anxiety or depressive symptoms, although its small magnitude and the cross-sectional design preclude causal interpretation. The findings demonstrate that several variables play substantial roles in eating attitudes and influence BMI in this population. Notably, resilience emerges as a significant psychological factor. However, further research is warranted to fully elucidate its role and impact. These insights provide valuable guidance for refining preoperative assessments. Full article

Background: Chatbots are becoming increasingly valuable in clinical settings, offering rapid access to medical information, aiding documentation, and improving perioperative patient education. Their adaptability makes them promising tools for personalized perioperative risk stratification (PRS) and anesthesia planning, but their definitive role remains uncertain. We aimed to evaluate chatbot performance in PRS compared to standard clinical judgment and to assess the certainty of the evidence supporting their use. Methods: This systematic review (PROSPERO ID: CRD42025642357) followed PRISMA extended and PRISMA-S guidelines. The population was defined according to the PICO framework: we included adult surgical patients undergoing anesthesia assessment (P), evaluated with LLM-based chatbots for perioperative risk stratification and anesthesia planning (I), compared with traditional clinician assessment (C), and extracted performance metrics (O). Comprehensive searches of PubMed, MEDLINE, Scopus, Embase, Google Scholar, Open Gray, ClinicalTrials.gov, WHO ICTRP, and Cochrane Library Central were conducted through January 2026. Risk of bias and study quality were assessed using PROBAST-AI, RoB-2, and ROBINS-I. Certainty of the evidence was assessed using GRADE system. A random-effects meta-analysis of pooled chatbot accuracy was performed, with subgroup analyses by ASA status and perioperative risk stratification. A sensitivity analysis was performed with a leave-one-out exclusion test. Results: Eleven studies published between 2023 and January 2026 were included (N = 227,059 patients). Five prospective cohorts, two large retrospective cohorts, one randomized non-inferiority trial, and three non-clinical or mixed-methods studies were found. Meta-analysis showed that the pooled accuracy of LLM-based chatbots for AI–clinician concordance in perioperative risk stratification and ASA classification was 0.90 [95% CI: 0.42–0.99; 95% prediction interval 0.03–1.00]. Subgroup analyses indicated that the ASA status prediction subgroup reached a pooled accuracy of 0.91 (95% CI: 0.46 to 0.99), whereas the exploratory perioperative risk stratification subgroup showed an accuracy of 0.73 (95% CI: 0.10 to 0.98). Performance decreased with increasing patient complexity. Evidence is limited by small sample sizes, extreme sample size skew toward a single center, geographic bias, inconsistent outcome definitions and performance metrics, and incomplete reporting of adverse events. Most studies lacked prospective trial registration or robust control for confounding, and publication bias cannot be excluded. Conclusions: LLM-based chatbots show promising performance in routine perioperative risk stratification but remain unreliable in complex cases, with potential safety concerns. Given the overall very low GRADE certainty of evidence, these tools should be used as clinician-supervised decision support aids for routine ASA assessment, and should not be relied upon for autonomous use in complex cases or for general perioperative risk stratification. Other: This research received no external funding. PROSPERO ID: CRD42025642357. Full article

Artificial intelligence (AI) is increasingly integrated into emergency general surgery (EGS), where rapid diagnosis, accurate decision-making, and timely intervention are essential for improving patient outcomes. Recent advances in machine learning, deep learning, computer vision, and predictive analytics have enabled AI-assisted systems to support clinicians throughout the perioperative workflow. Current applications include radiologic image interpretation, diagnosis of acute abdominal conditions, surgical workflow recognition, intraoperative anatomical guidance, postoperative complication prediction, and intensive care monitoring. AI technologies may improve diagnostic accuracy, optimize operative planning, enhance surgical safety, and facilitate personalized perioperative management. In minimally invasive surgery, computer vision and real-time data analysis have shown promising results for intraoperative decision support and surgical education. However, important limitations remain, including concerns regarding data quality, algorithm transparency, ethical governance, regulatory approval, and implementation disparities between healthcare systems. In addition, much of the current evidence is derived from retrospective or highly specialized datasets, limiting broad clinical applicability. This narrative review summarizes the current clinical applications of AI in emergency general surgery and discusses emerging technologies, existing challenges, and future perspectives regarding the integration of AI into acute surgical care. Full article

Introduction: Remote education is increasingly used in ostomy care, but its components, timing, governance, and evaluation remain inconsistently defined. This study aimed to develop practice-oriented recommendations for implementing remote patient education for people living with an ostomy. Methods: An Italian expert consensus using a modified Delphi method and reported according to the ACCORD guidelines was conducted. An expert panel (n = 11), recruited nationally, included stomatherapists (n = 6) and people living with an ostomy (n = 5). Round 1 comprised a remotely conducted focus group to generate and refine statements informed by a targeted literature search. Rounds 2 and 3 were anonymous online surveys in which panelists rated statements on a four-point Likert scale and could provide comments or propose additional items. Consensus was predefined as ≥75% agreement. Results: Response rates were 100% across the three rounds (October–November 2025). The panel achieved consensus on 8 definitions and 14 statements, organized into six domains: (1) model of care and eligibility; (2) privacy and data protection; (3) program structure, outcomes, and evaluation; (4) educational content and teaching strategies; (5) timing, intensity, follow-up, and caregiver involvement; and (6) dignity, relational quality, and professional and organizational requirements. Recommendations supported a hybrid-by-default model with eligibility criteria, privacy-by-design using secure platforms and traceable documentation, structured programs with tailored multimodal content, staged pathways lasting 2–6 months after an initial in-person foundation, dignity-preserving options during remote encounters, professional training in communication and digital empathy, and integration into clinical planning and records. Conclusions: This consensus provides the first ostomy-specific, implementation-focused recommendations for standardizing remote patient education in Italy, with an emphasis on equity, privacy, dignity, evaluation, and workforce competencies. Full article

Hepatobiliary surgery is a technically complex subspecialty within general surgery, which requires a comprehensive understanding of complex liver and liver tumour anatomy. The current body of literature highlights the use of three-dimensional-printed liver models (3DPLMs) reconstructed from medical imaging datasets may improve clinician comprehension of patient-specific liver anatomy thus creating a useful tool for hepatobiliary surgical planning and clinician training. The purpose of this systematic review was to examine the clinical utility and feasibility of 3DPLMs in hepatobiliary surgical planning and clinical education and investigate whether these applications influence patient outcomes. Studies were retrieved from three electronic databases (ProQuest, PubMed and Scopus) according to predetermined eligibility criteria. In total, 25 eligible articles were identified, including 18 original research articles and seven case reports. An inductive content analysis approach suitable for heterogeneous bodies of literature was used to synthesise key concepts in this review. There are significant case report and descriptive evidence to support the use of 3DPLMs in clinical education, preoperative planning and intraoperative guidance of patient liver and tumour anatomy to improve hepatobiliary surgical decision making. The studies presented display a large variance in cost and times necessary for the production of 3DPLMs, as studies did not include the software, equipment and full expense of materials used. Additionally, studies concentrated on different aspects of the 3DPLMs production process making them not comparable. This review demonstrates the potential value of 3DPLMs in clinical education, preoperative planning and intraoperative guidance in hepatobiliary anatomy and surgery. Future studies, in particular, randomised controlled trials and experimental research are required to investigate the relationship between 3DPLMs and clinical education and surgical planning outcomes. Full article

Background: Cancer-associated thrombosis (CAT) is a leading cause of morbidity and mortality in cancer patients. Although international guidelines provide comprehensive recommendations for venous thromboembolism (VTE) prevention and treatment, the degree to which clinicians adhere to these guidelines in routine practice remains unclear, particularly in countries with limited national data such as Turkey. Methods: A cross-sectional, descriptive survey was conducted among oncology specialists (medical oncologists, radiation oncologists, and surgical oncologists) and cardiologists practicing across Turkey. A structured, case-based questionnaire comprising 21 multiple-choice questions was distributed electronically via SurveyMonkey. The questionnaire assessed perioperative VTE prophylaxis approaches, VTE risk assessment practices in ambulatory patients, primary and long-term secondary thromboprophylaxis preferences, acute VTE treatment strategies, and management of special clinical scenarios. Responses were analyzed using descriptive statistics and compared between oncologist and cardiologist groups. Results: A total of 84 physicians participated (34 oncologists [40.5%], 50 cardiologists [59.5%]). Perioperative and inpatient VTE prophylaxis practices were largely concordant with guideline recommendations, with 67.9% individualizing prophylaxis decisions and 66.7% initiating prophylaxis in hospitalized immobile patients when not contraindicated. However, only 33.7% routinely performed VTE risk assessment in ambulatory patients, and 64.6% did not use any validated risk scoring system. Low-molecular-weight heparin (LMWH) was the preferred agent for acute VTE treatment (72.6%), while direct oral anticoagulants (DOACs) gained preference in long-term secondary thromboprophylaxis (42.2%). No statistically significant differences were observed between oncologists and cardiologists across all survey items (all p > 0.05). Notably, 94.1% of respondents expressed a need to update their knowledge regarding CAT management. Conclusions: While oncologists and cardiologists in Turkey demonstrate general awareness of CAT guidelines, significant gaps persist in VTE risk stratification and primary prophylaxis for ambulatory cancer patients. The near-universal self-reported need for knowledge updates highlights the urgency for structured multidisciplinary education programs, integration of validated risk scoring tools into clinical workflows, and development of nationally adapted clinical practice guidelines. These findings reflect self-reported practices and may not fully represent actual clinical behavior; future studies incorporating medical record reviews or prescription data are needed to validate these observations. Full article

The introduction of minimally invasive surgery (MIS) marked a turning point in the history of medicine, driving one of the sharpest declines in surgical mortality and morbidity ever recorded—saving millions of lives and sparing an estimated one billion patients the suffering once inherent to large-incision surgery. Within a single generation, this once highly contested surgical innovation became the global standard of care, transforming surgical practice across disciplines and on a global scale. By every measure of public health, these outcomes place modern minimally invasive and robotic-assisted surgery as among the most consequential life-saving advances in modern medical history. This review examines the clinical impact and global dissemination of MIS, tracing its evolution from Camran Nezhat’s pioneering expansion of laparoscopy beyond diagnostics to complex therapeutic procedures across surgical disciplines. Drawing on decades of evidence across gynecology, general surgery, and urology, we show that MIS is associated with substantial reductions in perioperative mortality, major complications, blood loss, infections, thromboembolic events, postoperative pain, and length of hospital stay, while maintaining oncologic equivalence and improving functional and quality-of-life outcomes. Beyond these technical advances, MIS catalyzed a broader reimagining of surgery itself, challenging long-standing norms rooted in large-incision approaches and shifting the field toward precision, organ preservation, and pathology-directed intervention. These changes were accompanied by parallel advances in multiple domains, including in imaging, intraoperative visualization technologies, surgical anatomy, instrumentation, and nerve- and organ-sparing techniques—developments that collectively established the foundation for contemporary minimally invasive and robotic-assisted surgery. Collectively, these advances have contributed to the prevention of an estimated 10–20 million surgery-related deaths that would likely have occurred under the large-incision approaches of the past. Full article

Background: Health accessibility is a key determinant of equitable cancer care. In many countries, specialized oncology services are concentrated in urban and socioeconomically advantaged regions, forcing many patients to travel long distances for treatment. Consequently, geographic and social characteristics may be impactful in determining cancer healthcare outcomes. Objective: The aim of this study was to evaluate the association between the municipal-level Social Progress Index (SPI) and geographic travel burden, stage at diagnosis, treatment, and survival in patients with pancreatic cancer in São Paulo state, Brazil. Methods: We conducted a population-based study using data from “Fundação Oncocentro” on adults with pancreatic adenocarcinoma (2005–2025). The SPI (0–100 scale), a composite measure of municipal social and environmental development, was the primary exposure. It is structured into 3 dimensions and 12 components: Basic Human Needs (nutrition, medical care, water and sanitation, housing, safety); Foundations of Well-being (education, information access, health, environmental quality); and Opportunity (rights, freedom of choice, social inclusion, higher education). Municipal residence and cancer center locations were geocoded, and travel distance (km) was estimated. Multivariable Cox, logistic, and linear regression models assessed associations between SPI and overall survival, stage IV at diagnosis, surgery, and travel distance. Results: A total of 13,478 patients were included (mean follow-up 15.1 ± 27.2 months; mean age 62.3 years; 50.4% male). Stage IV disease was frequent (46.3%), and surgery was performed in 33% of cases. Over half of patients (53.2%) traveled more than 10 km for treatment. Increasing SPI was strongly associated with shorter travel distance (β −62.6 km per SPI unit; p < 0.001) and higher odds of surgery (OR 1.04; p < 0.001) and remained independently associated with a higher likelihood of undergoing surgical treatment (adjusted OR 1.04; p < 0.001). The proportion of stage IV disease did not decrease with increasing SPI and was slightly higher in the highest quartile (49.3%). In survival analysis, SPI demonstrated a protective effect in univariate modeling (HR 0.987; p < 0.001), but lost significance in multivariable analysis (p = 0.125). Travel burden was not retained as an independent predictor of survival after adjustment. Conclusions: Municipal-level SPI was a strong determinant of healthcare access and the likelihood of receiving surgical treatment for pancreatic cancer. Social and geographic vulnerability directly influence care pathways, revealing structural inequities in access to treatment. SPI-based stratification may serve as a practical tool to identify priority regions for transport support and equitable allocation of oncology services. Full article

Background: The objective of the current study is to identify the empathy levels and attitudes of nurses who work in surgical clinics toward patients with disabilities and to investigate the correlation between these two variables. Methods: This cross-sectional, correlational-predictive study included 305 nurses working in the surgical clinics of a Training and Research Hospital affiliated with a Health Sciences University in Türkiye. The research data were collected using a Sociodemographic Information Form, the Jefferson Empathy Scale (JES), and the Disability Attitudes in Health Care Scale (DAHC) between February and April 2026. Descriptive statistics, Pearson’s correlation analysis, POMP (%) score graph, eta-squared (η²) effect size, independent samples t-test, one-way analysis of variance (ANOVA), and hierarchical multiple linear regression analysis were used to evaluate the data. Results: Participants scored an average of 102.8 points on the JES and 55.4 points on the DAHC. This value falls within Jefferson’s “moderately high empathy” range (88.6–105.7), and the mean attitude score was also found to increase positively. A positive, moderate, and statistically significant correlation was found between the JES and the DAHC (r = 0.520, p < 0.001). In the regression analysis, the explanatory power increased from 12.8% in Model 1 to 18.8% in Model 2 and to 35% in Model 3 (ΔR² = 0.160). Considering the relative importance order of the independent variables in the final model of the regression analysis, it is understood that the strongest predictor of empathy was the DAHC (β = 0.430), followed by professional experience (β = 0.149) and education level (β = −0.145). Conclusions: The present study found that attitude, education level, and professional experience were the main factors affecting the empathy level. A positive correlation was identified between empathy and attitude. Hence, the accessibility of patients with disabilities to intensive and stressful clinics such as surgical clinics, the organization of in-service training, and the integration of multidisciplinary teamwork can create equal opportunities in health care for patients with disabilities. Full article

Background/Objectives: The global adoption of minimally invasive surgery has generated extensive video repositories, creating new opportunities for data-driven surgical education and quality assessment. Automated surgical phase recognition enables objective trainee evaluation, standardized competency assessment, and systematic procedural documentation. However, class imbalance in surgical workflows, where certain phases comprise 30–35% of frames while others represent only 5–10%, remains a significant challenge. This imbalance causes models to underperform on underrepresented yet clinically important phases. Methods: A retrospective analysis of laparoscopic cholecystectomy videos is performed with the implementation of a frame—based deep learning framework to develop and validate a surgical phase recognition pipeline based on ResNet-50 architecture with transfer learning. The model was designed to extract features from surgical video frames and classify them into seven distinct phases, without incorporating temporal context. We used the Cholec80 dataset and applied class balancing techniques to address inherent class imbalance. Results: The model achieved a mean balanced accuracy of 91.80% across five folds with consistent performance across all surgical phases. Per-phase F1-scores ranged from 0.89 to 0.95, demonstrating balanced classification without significant performance degradation on underrepresented phases. The confusion matrix revealed prediction errors primarily among adjacent or visually similar phases, reflecting the inherent ambiguity of surgical phase transitions. In practical terms, the model correctly identified the surgical phase in more than 9 out of 10 frames, enabling reliable automated segmentation of the operative workflow. Conclusions: This study demonstrates that artificial intelligence can reliably analyze surgical video data, achieving consistent and accurate phase recognition in laparoscopic cholecystectomy. Full article

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, with growing applications in pediatric cardiology. AI techniques, particularly machine learning and deep learning, enable the analysis of complex and heterogeneous data, supporting diagnosis, risk stratification, and clinical decision-making. This paper provides an overview of current AI applications in this field, discusses existing challenges, and explores future perspectives. In pediatric cardiology, AI has shown promising results across multiple domains. In electrocardiography, AI algorithms improve diagnostic accuracy and enable early detection of cardiac conditions, even in asymptomatic patients, while facilitating telecardiology-based care pathways. In cardiac auscultation, AI-assisted digital stethoscopes enhance the distinction between innocent and pathological murmurs, supporting primary care physicians and optimizing referral to pediatric cardiologic centers. Multimodality imaging represents one of the most advanced areas of AI applications. In echocardiography, magnetic resonance and computed tomography, AI improves image acquisition, view classification, and automated quantification, contributing to more standardized and reproducible assessments. Additionally, emerging technologies such as virtual reality, integrated with AI, offer innovative tools for education, surgical planning, and patient-specific modelling. Despite these advances, several limitations remain, including limited availability of large pediatric datasets, challenges in model generalizability and issues related to interpretability and integration into clinical workflows. In conclusion, AI represents a powerful complementary tool in pediatric cardiology, with the potential to improve diagnostic accuracy, optimize healthcare resources and support the transition toward precision medicine. Full article

Background/Objectives: Orofacial myofunctional therapy (OMT) is a system of targeted neuromuscular exercises and behavioral retraining intended to optimize tongue, lip, jaw, and airway function during rest, breathing, swallowing, and sleep. Historically associated with tongue thrust and abnormal swallowing, OMT is now applied across an expanding range of clinical contexts, including sleep-disordered breathing (SDB), tongue-tie rehabilitation, orthodontic stability, and perioperative functional recovery. As its use has broadened, persistent questions have followed: what is myofunctional therapy, where did it originate, and how did a set of oral exercises evolve into an intervention increasingly integrated with airway health, sleep medicine, and surgical care? Methods: This article presents a narrative historical review with a perspective component, synthesizing foundational literature, interdisciplinary contributions, and selected contemporary evidence to examine the evolution of OMT from ancient functional practices to modern clinical science. It is written to trace recurring clinical observations, shifts in educational frameworks, and key inflection points that shaped how OMT has been taught and applied over time. Results: OMT did not emerge from randomized controlled trials or standardized protocols. It arose from repeated clinical encounters with patients with atypical craniofacial development, relapse of structural correction, persistent mouth breathing, and/or unresolved swallowing and speech dysfunction despite technically successful treatment. These patterns suggested that anatomy alone could not account for outcome variability. Over time, clinical attention expanded beyond isolated tongue function to include breathing patterns, posture, neuromuscular tone, and airway behavior. In the past two decades, controlled trials, cohort studies, and systematic reviews have supported selected applications of OMT, particularly in SDB and adjunctive airway care, while also revealing ongoing challenges related to training variability, terminology, scope of practice, and standardization. Conclusions: OMT has historically been described as a system of targeted neuromuscular and behavioral interventions aimed at modifying orofacial rest posture and function. Over time, the field has expanded beyond localized muscle retraining toward a broader functional framework that integrates airway physiology, craniofacial growth, sleep, and interdisciplinary rehabilitation. Full article