Search Results (37)

Background: The rapid advancement in artificial intelligence (AI) has fundamentally reshaped gut microbiome research by enabling high-resolution analysis of complex, high-dimensional microbial communities and their functional interactions with the human host. Objective: This narrative review aims to synthesize recent methodological advances in AI-driven gut microbiome research and to evaluate their translational relevance for therapeutic optimization, personalized nutrition, and precision medicine. Methods: A narrative literature review was conducted using PubMed, Google Scholar, Web of Science, and IEEE Xplore, focusing on peer-reviewed studies published between approximately 2015 and early 2025. Representative articles were selected based on relevance to AI methodologies applied to gut microbiome analysis, including machine learning, deep learning, transformer-based models, graph neural networks, generative AI, and multi-omics integration frameworks. Additional seminal studies were identified through manual screening of reference lists. Results: The reviewed literature demonstrates that AI enables robust identification of diagnostic microbial signatures, prediction of individual responses to microbiome-targeted therapies, and design of personalized nutritional and pharmacological interventions using in silico simulations and digital twin models. AI-driven multi-omics integration—encompassing metagenomics, metatranscriptomics, metabolomics, proteomics, and clinical data—has improved functional interpretation of host–microbiome interactions and enhanced predictive performance across diverse disease contexts. For example, AI-guided personalized nutrition models have achieved AUC exceeding 0.8 for predicting postprandial glycemic responses, while community-scale metabolic modeling frameworks have accurately forecast individualized short-chain fatty acid production. Conclusions: Despite substantial progress, key challenges remain, including data heterogeneity, limited model interpretability, population bias, and barriers to clinical deployment. Future research should prioritize standardized data pipelines, explainable and privacy-preserving AI frameworks, and broader population representation. Collectively, these advances position AI as a cornerstone technology for translating gut microbiome data into actionable insights for diagnostics, therapeutics, and precision nutrition. Full article

Objectives: Telemedicine has rapidly expanded in oral and maxillofacial surgery (OMFS), especially during the COVID-19 pandemic, but its specific roles and limitations across the care pathway remain unclear. This narrative review aimed to map telemedicine modalities and indications in OMFS, summarize reported outcomes, and identify priorities for future research. Methods: A narrative synthesis was undertaken after a systematic search of medical and engineering databases to 10 October 2025. Studies applying telemedicine, telehealth, telepresence or teleradiology to OMFS practice were eligible, including trials, observational cohorts, technical reports and surveys. Data were extracted in duplicate and organized thematically; heterogeneity precluded meta-analysis. Results: Fifty studies met the inclusion criteria. Telemedicine was mainly used for preoperative consultation and triage, postoperative follow-up, trauma teleradiology and tele-expertise, oncologic and oral medicine follow-up, temporomandibular disorders, and education or humanitarian work. In low-risk outpatient and postoperative settings, remote consultations showed high concordance with in-person plans, similar complication or reattendance rates, reduced travel, and high satisfaction. In trauma networks, telemedicine supported timely triage and reduced unnecessary inter-hospital transfers. Evidence in oral oncology and complex mucosal disease was more cautious, favouring hybrid models and escalation to face-to-face assessment. Data on cost-effectiveness and impacts on equity were limited. Conclusions: Telemedicine in OMFS has moved from niche innovation to a pragmatic adjunct across the clinical pathway. Current evidence supports its use for selected pre- and postoperative care and trauma triage within risk-stratified hybrid models, while underscoring the need for stronger comparative and implementation studies, clear governance on equity and data protection, and alignment with wider digital and AI-enabled health systems. Full article

Cardiovascular–Kidney–Metabolic (CKM) syndrome arises from interrelated cardiovascular, renal, and metabolic pathways that require coordinated therapeutic strategies. This narrative review synthesizes recent systematic reviews, meta-analyses, and original studies to evaluate the translational application of lifestyle medicine (LM) for CKM management. Evidence indicates that LM interventions targeting the six pillars of practice (nutrition, physical activity, stress management, sleep, social support, and avoidance of risky substances) can improve blood pressure, lipid profiles, glycemic control, and weight, with benefits that complement or at times rival pharmacotherapy. We outline opportunities at the LM–drug interface, including sodium-glucose cotransporter-2 inhibitors and nutrient-stimulated hormone agents such as GLP-1 and GIP, and highlight the need to test synergy and sequencing with LM. Persistent implementation barriers include prioritization of drug-centric care and limited protocolized delivery; the AHA 5A model and digital health tools, including wearables that enable real-time feedback, provide practical routes for integration. Marginalized populations carry a disproportionate burden of CKM, underscoring the need for equitable, culturally tailored approaches. Sex-specific gaps, particularly in post-menopausal lipid metabolism and insulin sensitivity, point to the promise of genomic and nutraceutical personalization. Future work should use preregistered, adequately powered multimodal trials to establish durable, scalable pathways for CKM care. Full article

Cardiovascular Disease (CVD) remains the leading cause of morbidity and mortality worldwide. Despite significant advances in diagnosis and treatment, the global burden of CVD remains high, underscoring the crucial need for more effective and comprehensive prevention strategies. This narrative overview aims to critically evaluate the current pillars of cardiovascular prevention, identify the gaps in approaches and outline promising future directions. Challenges and barriers in lifestyle adherence and pharmacological management are addressed, while the increasing role of non-traditional and emerging risk factors is discussed. Future directions include maximizing the value of digital health to improve patient engagement and adherence, adopting precision medicine to refine risk stratification and implementing public health policies for population-level interventions. The optimization of cardiovascular prevention requires a multi-level approach that integrates clinical strategies with personalized solutions and environmental policies to ultimately reduce the global impact of CVD. Full article

Introduction: Chest drainage is central to thoracic surgery, pleural medicine, and emergency care, yet practice remains heterogeneous in tube caliber, access, suction, device selection, and removal thresholds. This narrative review aims to synthesize evidence and translate it into guidance. Materials and Methods: We performed a narrative review with PRISMA-modeled transparency. Using backward citation from recent comprehensive overviews, we included randomized trials, meta-analyses, guidelines/consensus statements, and high-quality observational studies. We extracted data on indications, technique, tube size, analog versus digital drainage, suction versus water-seal drainage, removal criteria, and key pleural conditions. Due to heterogeneity in device generations, suction targets, and outcomes, we synthesized the findings qualitatively according to converged evidence. Results: After lung resection, single-drain strategies, early use of water-seal, and standardized removal at ≤300–500 mL/day reduce pain and length of stay without increasing the need for reintervention; digital systems support objective removal using sustained low-flow thresholds (approximately 20–40 mL/min). Small-bore (≤14 Fr) Seldinger catheters perform comparably to larger tubes for secondary and primary pneumothorax and enable ambulatory pathways. In trauma, small-bore approaches can match large-bore drainage in stable patients when paired with surveillance and early escalation of care. For pleural infection, image-guided drainage, combined with fibrinolytics or surgery, is key. Indwelling pleural catheters provide relief comparable to talc in dyspnea associated with malignant effusions in patients with non-expandable lungs. Complications are mitigated by ultrasound guidance and avoiding abrupt high suction after chronic collapse; however, these strategies must be balanced against risks of malposition, occlusion or retained collections, prolonged air leaks, and device complexity, which demand protocolized escalation and team training. Conclusions: Practice coalesces around three pillars—right tube, right system, proper criteria. Adopt standardized pathways, device-agnostic thresholds, and volume or airflow criteria. Trials should harmonize “seal” definitions and validate telemetry-informed removal strategies. Full article

Intellectual disability (ID)—termed intellectual developmental disorder in DSM-5-TR and disorders of intellectual development in ICD-11—is a heterogeneous spectrum of neurodevelopmental conditions characterized by impairments in general intellectual abilities and adaptive functioning, with onset during the developmental period. Precise and early recognition of ID can alter developmental trajectories by clarifying prognosis, guiding targeted genetic and medical investigations, initiating time-sensitive interventions, preventing diagnostic overshadowing, and informing educational planning and family counseling. This narrative review synthesizes contemporary diagnostic standards from DSM-5-TR and ICD-11, recent updates in epidemiology, and an aetiology-oriented framework for classifying “types” of intellectual disability. It further outlines current best practices in diagnostic work-up—including developmental surveillance and screening, standardized cognitive and adaptive assessments, genomic testing (microarray, exome/genome sequencing, and syndrome-specific assays), selective metabolic studies, and neuroimaging or EEG when indicated. Additionally, the review explores culturally responsive and equitable assessment, special diagnostic contexts (such as profound impairment, sensory and motor confounds, and bilingualism), and the frequent co-occurrence of other neurodevelopmental and medical disorders. The discussion concludes with practical, stepwise diagnostic pathways that clinicians can readily apply and anticipates emerging frontiers—such as genomic medicine and digital phenotyping—that promise to enhance diagnostic yield and clinical utility in the near future. Full article

Background: Over 80–90% of antimicrobial use occurs in primary health care, underscoring the need for specific data from this sector to inform practices and interventions to improve antimicrobial use. This study aimed to identify a wide range of research instruments in primary health care and qualitatively describe their structure, scope, and content. Methods: For the narrative review, we reviewed Medline (inception–November 2023) and agency/network websites to identify surveys on antimicrobial use prevalence in LMIC primary care. We applied no language restrictions and extracted survey instruments from publications or requested them from authors when unavailable. Results: We identified 450 studies and extracted 42 survey instruments issued between 1993 and 2023, all but one post-2000. These covered both multi-country (16.7%) and country-specific implementations across all WHO regions. Sampling units included households/consumers (24/42, 57.2%), health professionals (14/42, 33.3%), drug sellers (3/42, 7.1%), and bulk sales data (1/42, 2.4%). Surveys typically captured antimicrobial type, prescription status, and reason for use; AWaRe classification was mentioned only once. We found 13 stand-alone protocols on antimicrobial use and 4 on general medicine use. Conclusions: We identified diverse tools for measuring antimicrobial use in LMICs, though many lacked protocols or analytic support. Surveys often focused solely on antibiotics, used paper-based methods, and rarely referenced the AWaRe classification. Future efforts should broaden the scope beyond antibiotics, leverage digital data systems, include implementation protocols and analytic tools, report standardized indicators, and adopt AWaRe-related variables as a core criterion to strengthen AMU monitoring in PHC. Full article

Background/Objectives: Immortality and anti-aging research is accelerating, with implications across medicine. This narrative review explores the biological principles, translational innovations, and ethical considerations at the intersection of aging and plastic surgery, reframed for a broad clinical audience. Methods: A narrative review of the literature from PubMed, clinical trials, and translational studies was conducted, with emphasis on regenerative medicine, stem cells, tissue engineering, gene editing, and longevity pharmacologics within the field of plastic and reconstructive surgery. Results: Key themes include (1) the biology of aging and epigenetic reprogramming, (2) esthetic and regenerative innovations with broader clinical significance, (3) emerging genetic and pharmacologic longevity strategies, (4) ethical and regulatory challenges, and (5) future directions such as nanotechnology, artificial intelligence, and digital immortality. Conclusions: Immortality remains an aspirational frontier, but innovations in regenerative science and longevity research offer opportunities for improving healthspans. Medicine as a whole must balance innovation with ethics, equity, and safety in translating these discoveries to patient care. Full article

Background: The last decade has seen transformative changes in burn care, driven by advances in pharmacology, regenerative medicine, surgical techniques, and digital technologies. As management strategies evolve beyond survival to encompass functional and esthetic recovery, this review consolidates current evidence to inform best practice. Methods: A comprehensive narrative review was conducted using PubMed to identify peer-reviewed English-language articles from the past 10 years relevant to acute and long-term burn management. Selection focused on high-level evidence, including randomized controlled trials, systematic reviews, and meta-analyses, emphasizing novel and evolving clinical interventions. Results: Key advances include the integration of propranolol and oxandrolone for metabolic modulation; enzymatic debridement agents such as NexoBrid^®; regenerative approaches like epidermal cell sprays (e.g., RECELL^®) and dermal substitutes (e.g., Integra^®, MatriDerm^®, NovoSorb^® BTM); and innovations in scar modulation, notably fractional CO₂ laser therapy. The emergence of 3D bioprinting, and artificial intelligence further supports a shift toward precision burn medicine. Conclusions: Burn management is evolving from protocol-driven to patient-centred care, underpinned by high-quality evidence and technological innovation. The integration of systemic, local, and rehabilitative strategies is improving outcomes in survival, function, and quality of life. Ongoing challenges include cost, access, and translation of novel therapies into widespread clinical practice. Full article

Background: Artificial intelligence (AI), the overarching field that includes machine learning (ML) and its subfield deep learning (DL), is rapidly transforming clinical research by enabling the analysis of high-dimensional data and automating the output of diagnostic and prognostic tests. As clinical trials become increasingly complex and costly, ML-based approaches (especially DL for image and signal data) offer promising solutions, although they require new approaches in clinical education. Objective: Explore current and emerging AI applications in oncology and cardiology, highlight real-world use cases, and discuss the challenges and future directions for responsible AI adoption. Methods: This narrative review summarizes various aspects of AI technology in clinical research, exploring its promise, use cases, and its limitations. The review was based on a literature search in PubMed covering publications from 2019 to 2025. Search terms included “artificial intelligence”, “machine learning”, “deep learning”, “oncology”, “cardiology”, “digital twin”. and “AI-ECG”. Preference was given to studies presenting validated or clinically applicable AI tools, while non-English articles, conference abstracts, and gray literature were excluded. Results: AI demonstrates significant potential in improving diagnostic accuracy, facilitating biomarker discovery, and detecting disease at an early stage. In clinical trials, AI improves patient stratification, site selection, and virtual simulations via digital twins. However, there are still challenges in harmonizing data, validating models, cross-disciplinary training, ensuring fairness, explainability, as well as the robustness of gold standards to which AI models are built. Conclusions: The integration of AI in clinical research can enhance efficiency, reduce costs, and facilitate clinical research as well as lead the way towards personalized medicine. Realizing this potential requires robust validation frameworks, transparent model interpretability, and collaborative efforts among clinicians, data scientists, and regulators. Interoperable data systems and cross-disciplinary education will be critical to enabling the integration of scalable, ethical, and trustworthy AI into healthcare. Full article

Background: Plant-based diets are associated with reduced risk of chronic diseases and improved health outcomes. However, sustaining dietary changes remains challenging. Digital interventions—including virtual culinary medicine programs, web-based nutrition coaching, SMS and email reminders, mobile application–based self-management, and hybrid community programs—offer promising strategies to support behavior change, enhance cooking skills, and improve dietary adherence. These approaches are relevant for both healthy individuals and those living with chronic conditions. Methods: We conducted a narrative review of studies published between 2000 and 2025 in PubMed/MEDLINE, Scopus, and Web of Science, supplemented with manual searches. Included studies comprised randomized controlled trials, quasi-experimental designs, feasibility studies, and qualitative research. Interventions were categorized by modality (SMS, email, web platforms, mobile apps, virtual culinary programs, and hybrid formats) and population (healthy adults, patients with chronic diseases). Outcomes examined included dietary quality, self-efficacy, psychosocial well-being, and program engagement. Results: Most studies reported improvements in dietary quality, cooking skills, nutrition knowledge, and psychosocial outcomes. Virtual cooking programs enhanced dietary adherence and engagement, particularly among individuals at cardiovascular risk. Digital nutrition education supported behavior change in chronic disease populations, including patients with multiple sclerosis. SMS and email reminders improved self-monitoring and participation rates, while mobile applications facilitated real-time feedback and goal tracking. Hybrid programs combining online and in-person components increased motivation, social support, and long-term adherence. Reported barriers included limited technological access or skills, lack of personalization, and privacy concerns. Conclusions: Virtual culinary medicine programs and other digital microinterventions—including SMS, email, web, mobile, and hybrid formats—are effective tools to promote plant-based diets. Future interventions should focus on personalized, accessible, and hybrid strategies, with attention to underserved populations, to maximize engagement and sustain long-term dietary change. Full article

Artificial intelligence (AI), as an emerging interdisciplinary field dedicated to simulating and extending human intelligence, is increasingly integrating into the domain of infectious disease medicine with unprecedented depth and breadth. This narrative review is based on a systematic literature search in databases such as PubMed and Web of Science for relevant studies published between 2018 and 2025, with the aim of synthesizing the current landscape. It demonstrates transformative potential, particularly in the realm of diagnostic assistance. Confronting global challenges such as pandemic control, emerging infectious diseases, and antimicrobial resistance, AI technologies offer innovative solutions to these pressing issues. Leveraging its robust capabilities in data mining, pattern recognition, and predictive analytics, AI enhances diagnostic efficiency and accuracy, enables real-time monitoring, and facilitates the early detection and intervention of outbreaks. This narrative review systematically examines the application scenarios of AI within infectious disease diagnostics, based on an analysis of recent literature. It highlights significant technological advances and demonstrated practical outcomes related to high-throughput sequencing (HTS) for pathogen surveillance, AI-driven analysis of digital and radiological images, and AI-enhanced point-of-care testing (POCT). Simultaneously, the review critically analyzes the key challenges and limitations hindering the clinical translation of current AI-based diagnostic technologies. These obstacles include data scarcity and quality constraints, limitations in model generalizability, economic and administrative burdens, as well as regulatory and integration barriers. By synthesizing existing research findings and cataloging essential data resources, this review aims to establish a valuable reference framework to guide future in-depth research, from model development and data sourcing to clinical validation and standardization of AI-assisted infectious disease diagnostics. Full article

Background: Digital Pathology (DP) and Artificial Intelligence (AI) have strongly developed in recent years, especially in pathology, with a high interest in dermatopathology. Accelerated by the COVID-19 pandemic, DP and AI are now integrated in pathology, research and education, bringing value to histopathological diagnoses, telepathology and personalized medicine. This narrative review presents a comprehensive literature review by defining three research directions, using scientometric analysis, of the current state of DP and AI in pathology and dermatopathology. Methods: The research was conducted through the Pubmed and Web of Science databases, within the research period of January 2019–July 2025: a two-phase methodology. Four independent pathologists selected the articles in accordance with the inclusion and exclusion criteria, and the synthesis of the articles was based on three research directions. Results: The research shows that CNN (Convolutional Neural Network), AI powered diagnostic platforms and telepathology strongly contribute to increasing the speed and accuracy of diagnostics, especially on cutaneous malignant skin tumors. There are still several challenges and limitations in terms of validation, interoperability, initial high implementation costs, ethics and transparency in AI and equity in healthcare. Conclusions: DP and AI are essential pillars of modern dermatopathology, with a high necessity of standardization, regulation and a multidisciplinary approach. Full article

Low back pain (LBP) remains one of the most prevalent and disabling musculoskeletal conditions globally, with profound social, economic, and healthcare implications. The rising incidence and chronic nature of LBP highlight the need for more objective, personalized, and effective approaches to assessment and rehabilitation. In this context, bioengineering has emerged as a transformative field, offering novel tools and methodologies that enhance the understanding and management of LBP. This narrative review examines current bioengineering applications in both diagnostic and therapeutic domains. For assessment, technologies such as wearable inertial sensors, three-dimensional motion capture systems, surface electromyography, and biomechanical modeling provide real-time, quantitative insights into posture, movement patterns, and muscle activity. On the therapeutic front, innovations including robotic exoskeletons, neuromuscular electrical stimulation, virtual reality-based rehabilitation, and tele-rehabilitation platforms are increasingly being integrated into multimodal treatment protocols. These technologies support precision medicine by tailoring interventions to each patient’s biomechanical and functional profile. Furthermore, the incorporation of artificial intelligence into clinical workflows enables automated data analysis, predictive modeling, and decision support systems, while future directions such as digital twin technology hold promise for personalized simulation and outcome forecasting. While these advancements are promising, further validation in large-scale, real-world settings is required to ensure safety, efficacy, and equitable accessibility. Ultimately, bioengineering provides a multidimensional, data-driven framework that has the potential to significantly improve the assessment, rehabilitation, and overall management of LBP. Full article

Background: Bipolar disorder (BD) is a chronic and disabling psychiatric condition characterized by recurring episodes of mania, hypomania, and depression. Despite the availability of mood stabilizers, antipsychotics, and antidepressants, long-term management remains challenging due to incomplete symptom control, adverse effects, and high relapse rates. Methods: This paper is a narrative review aimed at synthesizing emerging trends and future directions in the pharmacological treatment of BD. Results: Future pharmacotherapy for BD is likely to shift toward precision medicine, leveraging advances in genetics, biomarkers, and neuroimaging to guide personalized treatment strategies. Novel drug development will also target previously underexplored mechanisms, such as inflammation, mitochondrial dysfunction, circadian rhythm disturbances, and glutamatergic dysregulation. Physiological endophenotypes, such as immune-metabolic profiles, circadian rhythms, and stress reactivity, are emerging as promising translational tools for tailoring treatment and reducing associated somatic comorbidity and mortality. Recognition of the heterogeneous longitudinal trajectories of BD, including chronic mixed states, long depressive episodes, or intermittent manic phases, has underscored the value of clinical staging models to inform both pharmacological strategies and biomarker research. Disrupted circadian rhythms and associated chronotypes further support the development of individualized chronotherapeutic interventions. Emerging chronotherapeutic approaches based on individual biological rhythms, along with innovative monitoring strategies such as saliva-based lithium sensors, are reshaping the future landscape. Anti-inflammatory agents, neurosteroids, and compounds modulating oxidative stress are emerging as promising candidates. Additionally, medications targeting specific biological pathways implicated in bipolar pathophysiology, such as N-methyl-D-aspartate (NMDA) receptor modulators, phosphodiesterase inhibitors, and neuropeptides, are under investigation. Conclusions: Advances in pharmacogenomics will enable clinicians to predict individual responses and tolerability, minimizing trial-and-error prescribing. The future landscape may also incorporate digital therapeutics, combining pharmacotherapy with remote monitoring and data-driven adjustments. Ultimately, integrating innovative drug therapies with personalized approaches has the potential to enhance efficacy, reduce adverse effects, and improve long-term outcomes for individuals with bipolar disorder, ushering in a new era of precision psychiatry. Full article