Search Results (368)

Stroke remains a leading cause of death and long-term disability worldwide, making prevention strategies a global health priority. Emerging technologies—including artificial intelligence (AI), wearable devices, digital health applications, and drone-assisted emergency systems—are increasingly being explored to improve stroke prevention and early management. In primary prevention, machine learning models can identify individuals at high risk of stroke using clinical and behavioral data with high reported predictive accuracy, although most models are derived from retrospective, single-center datasets and still require prospective external validation. Digital devices and wearable technologies enable continuous monitoring of cardiovascular risk factors and support behavioral interventions aimed at reducing vascular risk. In secondary prevention, AI-based tools are being developed to predict stroke recurrence, identify modifiable risk factors, and detect patients at risk of poor medication adherence. In the acute setting, AI-assisted neuroimaging platforms are already integrated into clinical and telestroke workflows, supporting rapid triage and treatment decisions. In parallel, drone-based emergency systems may contribute to improved outcomes by reducing prehospital delays and facilitating telemedicine-based triage in remote or resource-limited settings, although current evidence is derived largely from out-of-hospital cardiac arrest pathways rather than stroke-specific trials. Although advanced neurotechnological systems capable of real-time neurophysiological monitoring and closed-loop neuromodulation exist in other neurological disorders, their role in stroke prevention remains largely theoretical. Overall, these technologies offer promising opportunities to reshape the continuum of stroke prevention and care, but further validation, integration into clinical workflows, and evidence of real-world effectiveness are required before widespread implementation. Full article

Nystagmus, the involuntary rhythmic oscillation of the eyes, is a critical diagnostic marker in vestibular medicine, distinguishing life-threatening central disorders such as stroke from benign peripheral conditions including Benign Paroxysmal Positional Vertigo (BPPV). Despite its clinical importance, accurate nystagmus assessment has long been constrained by expensive infrared video-oculography equipment such as videonystagmography, specialist dependency, and the episodic nature of vestibular symptoms that are often resolved before a clinical encounter. This review synthesizes approximately 50 papers published between 1952 and 2026 across four thematic domains: AI-driven nystagmus analysis, clinical medicine, smartphone and portable hardware innovations, and telemedicine and remote monitoring. On the AI front, classical machine learning models achieve up to 98.77% nystagmus recognition accuracy using ensemble methods, while deep learning frameworks spanning CNNs, U-Nets, LSTMs, and optical flow networks demonstrate clinical-grade slow-phase velocity measurement equivalent to gold standard video-oculography on standard smartphone RGB video. Large language and vision models including GPT-4V and Gemini 2.0 show early-stage promise as zero-shot triage tools but currently fall well below specialist-level diagnostic accuracy. Concurrently, portable hardware innovations ranging from 3D-printed goggle systems to ARKit-based smartphone applications are narrowing the accessibility gap, while telemedicine frameworks enable ictal recording and cloud-based specialist review outside the clinic. Across all domains, the common barriers to clinical translation are dataset scarcity for rare BPPV subtypes, sensitivity to ambient conditions, and the absence of explainable AI mechanisms. This review maps the current state of the field and identifies multimodal data fusion, prospective clinical validation, and interpretable AI as the critical next steps toward equitable, specialist independent vestibular diagnostics. Full article

With the prevalence of telemedicine and digital diagnosis, the security and integrity of medical images transmitted over open networks have become critical issues. To effectively defend against malicious tampering and ensure the reliability of diagnostic information, this study proposes a block-based image authentication and tamper detection framework (KPP-BA). This framework integrates key-dependent pixel permutation, hash-based message authentication code (HMAC)-SHA256 hash verification, and a parity-based 3-LSB minimal distortion embedding strategy. The core innovation lies in utilizing pseudo-random pixel permutation to disrupt spatial correlation within blocks, thereby effectively resisting collage and statistical analysis attacks. Furthermore, by combining the avalanche effect of HMAC-SHA256 with hybrid bit-plane feature extraction, the proposed method ensures extremely high sensitivity to subtle tampering. Experimental results on a dataset comprising 300 medical images demonstrate that the proposed method maintains superior visual quality while ensuring security, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 54.15 of 0.5 bit per pixel (bpp). Moreover, against various tampering attacks—including masking, copy–paste, circle masking, and collage—the method exhibits exceptional detection capabilities with an average detection accuracy of 99.99%. Compared with seven state-of-the-art methods, the proposed framework demonstrates significant advantages in both image fidelity and tamper localization precision, validating its feasibility and robustness for secure medical image transmission applications. Full article

Background: In hematologic malignancies, treatment allocation and outcome prediction are traditionally driven by clinical and biological parameters. However, growing evidence suggests that non-clinical factors—such as psychosocial context, caregiver availability, organizational support, and digital health integration—play a pivotal role in patients’ ability to tolerate and adhere to complex therapeutic pathways. The concept of “resilience” may offer a more comprehensive framework to capture this multidimensional readiness to treatment. Methods: We conducted a narrative review of the literature focusing on patient and caregiver resilience in hematologic settings. PubMed, Scopus, and Web of Science were searched for studies published in English over the last 15 years, addressing clinical, psychosocial, organizational, and contextual determinants influencing treatment tolerance, continuity of care, and outcomes in hematology. Results: the literature highlights resilience as a dynamic construct shaped by clinical fitness, psychological resources, caregiver competence, social and family context, healthcare system organization, and access to supportive technologies such as telemedicine. Several domains emerged as recurrent determinants of resilience, yet no standardized, integrated assessment tool is currently available in routine hematologic practice. Conclusions: Resilience in hematology should be reframed as a multidimensional, context-dependent construct extending beyond traditional clinical fitness. Incorporating resilience-oriented assessment into clinical workflows may improve treatment personalization, optimize resource allocation, and enhance patient- and caregiver-centered care. Future research should focus on developing pragmatic, clinically applicable tools to operationalize resilience in real-world hematologic settings. Full article

Artificial intelligence (AI) is increasingly influencing healthcare administration and clinical informatics by supporting disease diagnosis, clinical decision-making, treatment personalization, drug discovery, remote monitoring, public health surveillance, and hospital operations. However, the successful adoption of AI in healthcare depends not only on algorithmic performance, but also on its safe integration into clinical information systems, organizational workflows, and governance structures. This article presents a narrative critical review of recent advances in AI-driven healthcare, with a focus on four major domains: AI-enabled disease diagnosis, treatment personalization and clinical decision support, drug discovery and biomedical knowledge generation, and healthcare administration. Evidence from radiology, pathology, ophthalmology, dermatology, and cardiology shows that AI systems can achieve strong diagnostic performance in selected settings, while applications in electronic health records, natural language processing, telemedicine, and predictive analytics are increasingly used to support healthcare delivery and operational decision-making. At the same time, important barriers continue to limit real-world implementation, including fragmented data infrastructures, limited interoperability, poor data quality, algorithmic bias, lack of explainability, privacy and cybersecurity risks, unclear accountability, and insufficient external validation. This review critically examines these challenges and proposes a governance-oriented roadmap for responsible AI integration in healthcare administration and clinical informatics. The proposed roadmap emphasizes data readiness, model validation, workflow integration, institutional accountability, post-deployment monitoring, and workforce readiness. The findings suggest that AI can contribute to more efficient, accessible, and patient-centered healthcare only when it is implemented within trustworthy medical informatics ecosystems supported by ethical governance, human oversight, and continuous evaluation. Full article

Background: Arterial hypertension is one of the most prevalent chronic non-communicable diseases and a leading cause of cardiovascular morbidity and mortality worldwide. Its burden remains particularly high in rural and resource-limited settings, where access to healthcare is often constrained by shortages of healthcare professionals, geographical barriers, and underdeveloped infrastructure. These factors may contribute to delayed diagnosis, suboptimal disease control, and increased risk of complications. In this context, telemedicine has emerged as a useful approach to supporting hypertension management and improving access to care in rural populations. Methods: This study presents a narrative review of the literature focusing on the application of telemedicine in the management of arterial hypertension in rural populations. A structured literature search of PubMed, Scopus, and Web of Science databases was conducted for studies published between 2015 and 2025. The review included randomized controlled trials, systematic reviews, meta-analyses, and observational studies evaluating telemedicine interventions, including remote blood pressure monitoring, mobile health applications, and teleconsultations. Study selection was guided by relevance to the research objective, with particular attention to rural and resource-limited contexts. Results: Telemedicine interventions have been associated with improvements in blood pressure control, treatment adherence, and access to healthcare services. Evidence from randomized controlled trials and meta-analyses suggests modest reductions in systolic and diastolic blood pressure compared with standard care. However, a substantial proportion of the available evidence originates from studies conducted in general or mixed populations rather than exclusively rural settings. Therefore, the applicability of these findings to rural contexts remains limited and should be interpreted with caution. The effectiveness of telemedicine may vary depending on differences in healthcare infrastructure, resource availability, digital accessibility, and organizational models across healthcare systems. Integrated care approaches involving primary healthcare providers and specialist support may contribute to improved continuity of care, although their impact appears to be context-dependent. Key barriers include limited telecommunication infrastructure, digital literacy challenges, and difficulties in integrating telemedicine into routine clinical practice. Conclusions: Telemedicine may represent a useful approach to supporting hypertension management in rural populations. However, its implementation requires careful consideration of local healthcare systems, patient characteristics, and organizational context. Telemedicine should be viewed as a context-dependent strategy rather than a uniform solution. Further context-specific research is needed to evaluate the long-term clinical, organizational, and economic impact of telemedicine interventions in rural hypertension management. Full article

Emerging technologies offer significant opportunities for sustainable rural development; however, their applications have not been systematically mapped across all dimensions of sustainability. This scoping review aims to identify, classify, and synthesize the literature on emerging technologies in rural development, structured around four pillars: economic, social, environmental, and governance. Eligible studies included English-language scientific articles published between 2015 and 2025 that propose solutions based on emerging technologies in rural contexts, identified in the Web of Science Core Collection database, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Data extracted from the 129 eligible articles were synthesized in thematic tables and comparatively analyzed for each pillar. Results indicate an accelerated growth in publications after 2020, with machine learning, deep learning, and the Internet of Things dominating applications such as precision agriculture, telemedicine, and water management. Critical gaps persist in biodiversity monitoring, climate adaptation, elderly care services, and rural circular economy, with the governance pillar remaining the least represented. This study proposes an integrated framework and a knowledge map to guide future research and public policies toward balanced and sustainable rural transformation. Full article

Background/Objectives: The rapid expansion of telemedicine has reshaped healthcare delivery, positioning telenursing as essential for continuity of care and patient management. This scoping review maps current evidence on telecare nursing practices, examining organizational models, professional roles, and key clinical and organizational outcomes. Methods: The review was conducted across five international databases, following the methodological framework proposed by Arksey and O’Malley, the interpretive extension by Levac et al., and the Joanna Briggs Institute guidelines, with reporting aligned to PRISMA-ScR recommendations. The search identified 1760 records, of which 1219 remained after duplicate removal. After title and abstract screening and full-text evaluation, 25 studies met the inclusion criteria. Results: Telenursing was implemented across diverse clinical contexts, particularly in chronic disease management, oncology, postoperative care, and emergency settings. Evidence indicates improvements in symptom management, therapeutic adherence, quality of life, and complication reduction, suggesting positive clinical and organizational impacts. The literature highlights the need for advanced digital, communication, and relational competencies, emphasizing the importance of targeted professional training. Cross-cutting trends include enhanced continuity of care, greater patient autonomy, improved integration between hospital and community services, and reduced healthcare costs. Conclusions: This review provides an updated overview of telenursing applications, highlighting their adaptability across clinical settings and the expanding strategic role of nurses in digital care. The findings indicate a rapidly evolving field and emphasize the need for further research to strengthen organizational frameworks, define advanced competencies, and support the sustainable integration of telenursing into healthcare systems. Full article

Smartphone-based portable slit lamp microscopes are increasingly used as low-cost tools for anterior segment imaging in teleophthalmology, yet the literature combines heterogeneous study designs, comparator standards, and deployment contexts. Because the evidence base spans engineering reports, basic science, clinical validation studies, implementation research, and case-based telemedicine, we structured a narrative review rather than a pooled meta-analysis. We searched PubMed/MEDLINE, Embase, Scopus, Web of Science, Google Scholar, Cochrane Library, ScienceDirect, and DOAJ for literature available on or before 28 February 2026, supplemented by manual reference list screening and targeted retrieval of relevant technical standards. Peer-reviewed English original studies formed the core evidence base; contextual non-English and gray literature sources were retained only when explicitly labeled as non-core. To improve interpretability, the results were grouped by synthesis domain, clinical task, comparator standard, telemedicine scenario, and artificial intelligence (AI) dataset/validation characteristics. The highest-confidence evidence concerned nuclear cataract grading, tear film breakup time and corneal staining assessment, anterior chamber depth screening, tear meniscus height measurement, allergic conjunctival grading, and selected corneal disorders. Agreement with conventional slit lamp examination or anterior segment optical coherence tomography was generally moderate to high within task-specific comparisons, and telemedicine deployment was feasible for screening, follow-up, remote consultation, emergency triage, house visits, and outreach. However, illumination reporting remains inconsistent, explicit ISO-aligned dosimetry is sparse, and most AI studies remain retrospective, single-center, and device family-specific. Current evidence, therefore, supports smartphone-based portable slit lamp microscopes primarily as adjunctive teleophthalmology tools rather than replacements for comprehensive in-clinic microscopy. The synthesis clarifies where conclusions are supported by comparative validation data, where they remain exploratory, and which methodological gaps should be prioritized in future multicenter studies. Full article

Background: Behavioural therapy (BT), including Comprehensive Behavioural Intervention for Tics (CBIT), is an evidence-based first-line treatment for patients with tic disorders. However, access remains limited due to a shortage of trained providers, geographical barriers, costs, and high treatment burden for patients and families. Rapid advances in digital health technologies including telemedicine, web-based treatment platforms, and mobile applications offer new opportunities to expand access to BT for individuals with tic disorders across the lifespan. Methods: For the purpose of this narrative review, we conducted a literature search in PubMed, Europe PMC, and the Cochrane Library to identify relevant studies investigating the effectiveness of digital health treatment approaches in tic disorders. Results: A total of 16 original studies were included. Although the available evidence remains limited and heterogeneous, existing studies suggest that emerging technologies for delivering behavioural interventions for tic disorders, including telehealth-based CBIT, digital therapy platforms, and app-supported habit reversal training (HRT), are feasible, cost-effective, user-friendly, flexible, and safe. These approaches also appear effective for symptom monitoring and personalized treatment support in both pediatric and adult populations. Conclusions: Recent technological advances have the potential to reduce the treatment gap in tic disorders, provided that these approaches are implemented within rigorous, evidence-based, and ethically grounded frameworks. Full article

This bibliometric study examines the role of ubiquitous computing and intelligent systems in the treatment of mental and neurological disorders. Ubiquitous computing integrates computational intelligence into everyday environments, enabling seamless monitoring and support of patients. Intelligent systems, including wearable devices, environmental sensors, and mobile health applications, collect real-time data on behavior, physiology, and environmental factors. These systems support early detection of symptom changes, adherence to treatment, and crisis prediction through context-aware analysis. Artificial intelligence (AI) processes the collected data to generate personalized therapeutic feedback and notify healthcare providers when intervention is needed. In mental health care, intelligent environments can monitor mood, sleep, and social interaction patterns, providing valuable objective information about mental health status. In the case of neurological conditions such as Parkinson’s disease or epilepsy, intelligent systems facilitate movement tracking, seizure detection, and cognitive assessment outside of the clinical setting. Integration with electronic health records and telemedicine platforms ensures coordinated and responsive care. Ethical design, privacy protection, and patient consent remain key to successful implementation. In this way, ubiquitous computing is transforming care models by increasing autonomy, precision, and continuity in the treatment of complex neurodegenerative diseases, including those related to neurodegeneration in aging. Full article

Telemedicine can address access barriers in childhood obesity management by supporting continuity of care and caregiver engagement. This randomized controlled trial compared a telemedicine-based program with guideline-based usual care among 70 children with obesity (aged 5–15 years) and their caregivers, randomized to telemedicine (n = 35) or usual care (n = 35) for 6 months. The telemedicine program included online consultations, digital caregiver education, remote monitoring, and secure messaging via the SUTH application integrated with the hospital information system. The control group received standard outpatient care with routine counseling and printed materials; baseline characteristics were similar between groups. Baseline demographic and clinical characteristics were comparable between groups. After 6 months, both groups showed modest reductions in BMI; however, ANCOVA-adjusted analyses indicated no significant between-group difference in post-intervention BMI. Weight-for-height decreased in both groups, with a slightly greater percentage reduction in the telemedicine group. Caregiver satisfaction and knowledge were significantly higher in the telemedicine group at follow-up (all p < 0.01; knowledge p < 0.001). These findings suggest that telemedicine-based care may contribute to modest improvements in anthropometric outcomes while substantially enhancing caregiver knowledge and healthcare service satisfaction, supporting its role as a scalable adjunct in pediatric obesity management. Full article

This systematic review analyzes how the introduction and progressive deployment of 5G networks have influenced the evolution of telemedicine between 2014 and 2024, focusing on their impact on performance, accessibility, and the feasibility of advanced clinical applications across the pre-COVID-19, COVID-19, and post-COVID-19 periods. The review was conducted in accordance with PRISMA guidelines and included publications retrieved from SCOPUS, PubMed, and Web of Science using a PICO-based search strategy. Studies were selected based on predefined inclusion and exclusion criteria, and extracted data included clinical parameters, network characteristics such as bandwidth and latency, geographic setting, and type of telemedicine service. A total of 45 studies met the inclusion criteria, with most published between 2020 and 2024. The most frequently reported applications were telediagnosis, particularly robotic ultrasound, followed by telesurgery and teleconsultation. The low latency enabled by 5G networks supported complex telesurgical procedures over distances exceeding 5000 km, while in ultra-remote areas, hybrid solutions combining 5G and fiber-optic networks were often adopted to ensure stable connections. The integration of robotic platforms and AI-based tools further enhanced the precision and reliability of remote procedures. Overall, 5G technology has significantly advanced telemedicine by enabling real-time, high-quality care over long distances, improving access to specialist services and supporting more equitable and efficient digital healthcare delivery, particularly in underserved regions. Full article

Human pose estimation is a crucial task in computer vision with widespread applications in healthcare, rehabilitation, sports, and remote monitoring. In this paper, we propose a deep learning-based method for automatic human pose estimation and joint angle computation, tailored specifically for physiotherapy and telemedicine scenarios. Beyond pose estimation, the proposed method is able to compute angles between joints, enabling analysis of body alignment and posture. The proposed approach is built upon a customized skeleton with 25 anatomical keypoints and a dataset composed of over 150,000 annotated and augmented images derived from multiple open-source datasets. Experimental results demonstrate the effectiveness of the proposed method, achieving a mAP@50 of 0.58 for keypoint localization and 0.98 for object detection. Moreover, we demonstrate several real-world practical use cases in evaluating exercise correctness and identifying postural deviations by exploiting the proposed method, confirming that the proposed method can represent a promising approach for automated motion analysis, with potential impact on digital health, rehabilitation support, and remote patient care. Full article

The management of complex acute and chronic wounds remains a formidable challenge in modern medicine, underscoring the urgent need for advanced therapeutic strategies that accelerate healing, prevent infection, and promote functional tissue regeneration. Electrospun nanofibers have attracted considerable attention in the biomedical field due to their extracellular matrix-like architecture, high surface area, interconnected porosity, and tunable physicochemical composition, which drive advances in wound regeneration, tissue engineering, and biopolymer-based therapeutics. In wound healing, nanofibrous dressings composed of natural polymers such as chitosan, gelatin, collagen, and cellulose promote cell attachment and proliferation, support angiogenesis, and enable infection control while delivering bioactive agents, thereby addressing significant challenges related to inflammation, biocompatibility, and antimicrobial resistance. In tissue engineering, aligned and hierarchically organized scaffolds fabricated from biopolymers such as collagen, gelatin, chitosan, and cellulose enhance the guided orientation of cells, differentiation, and functional regeneration of neural, musculoskeletal, vascular, and skin tissues. In addition to their conventional regenerative applications, recent studies have demonstrated that electrospun biopolymer nanofibers can be used in multifunctional biomedical platforms, including smart and stimuli-responsive systems for drug delivery, biosensing, regenerative interfaces, and wearable medical technologies. The integrated constructs that incorporate diagnostic or therapeutic functionalities, hybrid fabrication approaches that combine 3D printing with electrospinning, and intelligent biopolymer frameworks that enable telemedicine, real-time physiological monitoring, and personalized regenerative therapies offer new opportunities for developing improved biomedical systems. Overall, these advances position electrospun nanofiber systems as promising biomaterials for next-generation biomedical innovation. This review summarizes recent progress in tissue-engineered scaffolds, wound dressings, fabrication strategies for integrative therapeutics, and wearable devices with transformative potential for biomedical applications. Finally, the review addresses significant challenges related to scalability and clinical translation. It offers perspectives on future directions, including the integration of artificial intelligence and the regeneration of complex skin appendages, which will shape the next generation of nanofiber-based wound-healing therapies. Full article