Search Results (361)

Background/Objectives. Intracranial hemorrhages (ICHs) require immediate diagnosis for optimal clinical outcomes. Artificial intelligence (AI) is considered a potential solution for optimizing neuroimaging under conditions of radiologist shortage and increasing workload. This study aimed to directly compare diagnostic effectiveness between standalone AI services and AI-assisted radiologists in detecting ICHs on brain CT. Methods. A prospective, multicenter comparative study was conducted in 67 medical organizations in Moscow over 15+ months (April 2022–December 2024). We analyzed 3409 brain CT studies containing 1101 ICH cases (32.3%). Three commercial AI services with state registration were compared with radiologist conclusions formulated with access to AI results as auxiliary tools. Statistical analysis included McNemar’s test for paired data and Cohen’s h effect size analysis. Results. Radiologists with AI assistance statistically significantly outperformed AI services across all diagnostic metrics (p < 0.001): sensitivity 98.91% vs. 95.91%, specificity 99.83% vs. 87.35%, and accuracy 99.53% vs. 90.11%. The radiologists’ diagnostic odds ratio exceeded that of AI by 323-fold. The critical difference was in false-positive rates: 293 cases for AI vs. 4 for radiologists (73-fold increase). Complete complementarity of ICH misses was observed: all 12 cases undetected by radiologists were identified by AI, while all 45 cases missed by AI were diagnosed by radiologists. Agreement between methods was 89.6% (Cohen’s kappa 0.776). Conclusions. Radiologists maintain their role as the gold standard in ICH diagnosis, significantly outperforming AI services. Error complementarity indicates potential for improvement through systematic integration of AI as a “second reader” rather than a primary diagnostic tool. However, the high false-positive rate of standalone AI requires substantial algorithm refinement. The optimal implementation strategy involves using AI as an auxiliary tool within radiologist workflows rather than as an autonomous diagnostic system, with potential for delayed verification protocols to maximize diagnostic sensitivity while managing the false-positive burden. Full article

Background/Objectives: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. Methods: HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. Results: During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, p = 0.0135). Conclusions: Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts. Full article

Obesity is a growing global health concern with widespread impacts on physical, psychological, and social well-being. Clinically, it is a major driver of type 2 diabetes (T2D), cardiovascular disease (CVD), non-alcoholic fatty liver disease (NAFLD), and cancer, reducing life expectancy by 5–20 years and imposing a staggering economic burden of USD 2 trillion annually (2.8% of global GDP). Despite its significant health and socioeconomic impact, earlier obesity medications, such as fenfluramine, sibutramine, and orlistat, fell short of expectations due to limited effectiveness, serious side effects including valvular heart disease and gastrointestinal issues, and high rates of treatment discontinuation. The advent of glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., semaglutide, tirzepatide) has revolutionized obesity management. These agents demonstrate unprecedented efficacy, achieving 15–25% mean weight loss in clinical trials, alongside reducing major adverse cardiovascular events by 20% and T2D incidence by 72%. Emerging therapies, including oral GLP-1 agonists and triple-receptor agonists (e.g., retatrutide), promise enhanced tolerability and muscle preservation, potentially bridging the efficacy gap with bariatric surgery. However, challenges persist. High costs, supply shortages, and unequal access pose significant barriers to the widespread implementation of obesity treatment, particularly in low-resource settings. Gastrointestinal side effects and long-term safety concerns require close monitoring, while weight regain after medication discontinuation emphasizes the need for ongoing adherence and lifestyle support. This review highlights the transformative potential of incretin-based therapies while advocating for policy reforms to address cost barriers, equitable access, and preventive strategies. Future research must prioritize long-term cardiovascular outcome trials and mitigate emerging risks, such as sarcopenia and joint degeneration. A multidisciplinary approach combining pharmacotherapy, behavioral interventions, and systemic policy changes is critical to curbing the obesity epidemic and its downstream consequences. Full article

This review explores the potential of magistral formulas (MFs) as a viable option to meet the needs of neonates, given the lack of adequate therapies for this vulnerable group. The scientific literature on medicines available for neonates is limited. The physiological differences between neonates and adults make it difficult to formulate these medicines. In addition, there are a variety of difficulties in conducting research on neonates: few clinical trials are performed, and there is frequent use of unauthorized medicines. Pharmacokinetics in neonates was investigated in comparison to adults, and different aspects of the absorption, distribution, metabolism, and excretion were observed. One of the main problems is the different pharmacokinetics between the two populations. It is necessary to promote and allow research related to pediatric drug design, approve a specific authorization for use in age-appropriate dosage forms, and improve the quality and availability of information on drugs. This study focused on the MFs typically used for pediatrics, specifically for neonates, analyzing the pharmaceutical forms currently available and the presence of indications and dosage recommendations of the European Medicines Agency. Medications were classified according to therapeutic group, as antihypertensives, corticosteroids, and antiepileptics. The use of off-label medicines remains high in neonatal intensive care units and in primary healthcare, besides in the preparation of MFs by pharmacists. The shortage of medicines specifically designed and approved for neonates is a serious problem for society. Neonates continue to be treated, on numerous occasions, with off-label medicines. Studies and research should be expanded in this vulnerable population group. Full article

Medical hydrogels represent a promising solution for the treatment of corneal diseases and trauma, offering potential to address the shortage of donor corneas. To meet the functional requirements of artificial corneas in tissue engineering, it is crucial to fabricate biomimetic structures with high optical transparency using 3D printing techniques. As fiber alignment during the printing process has a pronounced impact on light transmittance, precise control of the printing parameters is essential. This study focuses on the experimental optimization of 3D printing conditions for hydrogel materials to improve their physical properties, particularly optical clarity, thereby enhancing their suitability for artificial corneal applications. Collagen derived from bovine Achilles tendons was chosen due to its excellent printability. A series of controlled experiments were conducted to systematically investigate the influence of key process parameters on hydrogel transparency. The findings enabled the identification of an optimized parameter set that significantly improved the optical properties of the 3D-printed biomimetic corneal stroma. Additionally, cell seeding and culture assays confirmed the favorable biocompatibility of the developed material. Full article

Background and Objectives: The coronavirus disease 2019 (COVID-19) pandemic significantly impacted healthcare systems worldwide. As a result, remote areas such as the Penghu Islands have encountered unique challenges related to pediatric care. This study examined the effects of the pandemic on the emergency air medical transport (EAMT) of pediatric patients from the Penghu Islands to Taiwan. Materials and Methods: This retrospective study analyzed 40 pediatric patients who received EAMT from the Penghu Islands to Taiwan between January 2017 and December 2022. This study compared patients before and during the COVID-19 pandemic and focused on patient demographics, reasons for EAMT, and clinical outcomes. Due to the small sample size, non-parametric statistical methods were applied, including the Mann–Whitney U-test for continuous variables and Fisher’s exact test for categorical variables. Results: Among the 40 pediatric patients analyzed, the median age decreased from 3 years (IQR, 0–5 years) before the pandemic to 1 year (IQR, 0–5 years) during the pandemic. While the overall increase in hospital length of stay during the pandemic was not statistically significant, a significant prolongation was observed in preschool-aged children and neonates without trauma (20 days vs. 9 days; p < 0.05). The lack of specialist physicians became an increasingly prominent factor for EAMT during the pandemic (p = 0.056). The most common medical reasons for EAMT were critical illness (35%), neonatal diseases (30%), and neurological conditions (27.5%), with similar distributions across both time periods. Conclusions: The COVID-19 pandemic heightened existing healthcare disparities in the Penghu Islands, particularly by increasing reliance on EAMT due to a shortage of pediatric specialists. Hospital stays for preschool children and neonates significantly increased during the pandemic, suggesting delayed or prolonged care. These findings underscore the need to strengthen local pediatric infrastructure, decentralize specialist services, and improve emergency preparedness to better support vulnerable populations in remote areas during future public health emergencies. Full article

Background/Objectives: Non-Communicable Diseases (NCDs) place an excessive strain on health systems in disaster-affected settings and may lead to a parallel public health emergency lasting months or years after a disaster. Although NCDs are increasingly recognized as a major challenge in disasters and humanitarian emergencies, a dedicated and standardized response plan is missing, as well as a shortage of evidence-based guidelines for NCD management in theses contexts. Over the years, Emergency Medical Teams (EMTs) have traditionally been deployed to manage acute conditions such as trauma and infectious diseases that quickly impact health systems. However, greater attention is needed to address acute exacerbation of NCDs and to ensure continuity of care for people with chronic health needs in disasters and emergencies. Methods: We conducted a scoping review exploring the EMTs’ management of chronic NCDs during disasters and humanitarian emergencies, in order to identify the strategies adopted, the challenges faced, and the recommendations provided to address this health problem. The online databases PubMed, Scopus, and EBSCO were searched to identify relevant papers. Results: After screening the papers against the eligibility criteria, 17 publications were retrieved. Five different areas of intervention concerning EMTs and NCDs management were identified: (i) EMTs pre-departure preparation, operational time, and length of stay; (ii) EMTs staff composition and training; (iii) EMTs logistics; (iv) EMTs integration with local health services; (v) EMTs clinical data record. Conclusions: The findings emerging from this study showed that NCDs significantly impact disaster response in different settings, underlining the need to implement a range of EMTs activities to guarantee assistance for chronic health needs. In view of strengthening the ability of health systems to cope with the NCDs’ burden, the EMTs’ initiatives should be considered as a bridge between the support provided during the acute phase of an emergency and the continuation of care ensured by the system in its early recovery phase. Full article

Artificial intelligence, the simulation of human intelligence by computers and machines, has found its way into healthcare, helping surgeons, doctors, radiologists, and many more. However, over 80% of healthcare professionals consists of people working in allied health professions such as nurses, physiotherapists, and midwives. Considering the aging of the general population around the world, the workforce shortages in these occupations are especially crucial. As the COVID-19 pandemic demonstrated, globally, most healthcare systems are strained, and there is a consensus that current healthcare systems are not sustainable with the increasing challenges. AI is often viewed as one of the potential solutions for not only reducing the strain on the healthcare workforce, but also to sustain the current workforce. Still, most AI applications are being developed for the medical community and often allied health is overlooked or not even considered despite comprising a large proportion of the total workforce. In addition, the interest of the private sector to invest specifically in the allied health workforce is low since the financial incentive is low. This paper provides examples of AI solutions for seven important allied health professions. To increase the uptake of AI solutions in allied healthcare, AI companies need to connect more with professional associations and be as patient-oriented as many claim to be. There also needs to be more AI schooling for allied healthcare professionals to increase adoption of these AI solutions. Full article

The intestine is an important segment of the gastrointestinal tract, which is involved in complex processes that maintain the body’s normal homeostasis. It hosts a vast, diverse, and dynamic microbial community called the gut microbiota, which develops from birth. It has been observed that the gut microbiota is involved in essential physiological processes, including the development of the central nervous system via the gut microbiota–brain axis. An alteration of the gut microbiota can lead to serious health problems, including defective neurodevelopment. Thus, this paper aims to highlight the most recent advances in studies that focus on the link between the gut microbiota and the evolution of neurodevelopmental diseases in children. Currently, studies show that the use of drugs that stimulate and restore the gut microbiota (e.g., probiotics and prebiotics) have the potential to alleviate some of the symptoms associated with conditions such as Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, Tic Disorder, Tourette Syndrome, epilepsy, and Down Syndrome. In addition, due to the challenges associated with drug administration in children, as well as the widespread shortage of medications intended for pediatric use, researchers are working on the development of new delivery systems. Liposome-based systems or solid lipid nanoparticles have been safely used for drug delivery in various pediatric conditions, which may also indicate their potential for use in the administration of microbiota-modulating therapies. Full article

Purpose: This study aimed to refine the criteria for EndoART implantation regarding posterior corneal irregularity; to improve the selection of candidates for this synthetic alternative to endothelial keratoplasty. Methods: This study analyzed the impact of posterior corneal surface elevation differences; anterior chamber depth (ACD); and preoperative corneal pachymetry on the success of EndoART implant adhesion. Patients undergoing EndoART implantation at the Medical University of Vienna were assessed using OCT to measure corneal irregularities. Postoperative outcomes, including re-bubbling rates; implant adhesion; and visual acuity changes, were monitored. Results: EndoART successfully adhered in eyes with moderate posterior irregularities (elevation differences up to 204 µm). Severe irregularities (elevation differences > 200 µm) resulted in implant detachment. No significant correlation was found between corneal pachymetry or ACD and adhesion failure. Glaucoma devices and prior penetrating keratoplasty did not significantly affect adhesion. Some cases required re-bubbling, and patients reported pain reduction and moderate improvements in visual acuity. Conclusions: This study found that EndoART implantation can be successful despite posterior corneal irregularity. EndoART represents a viable solution for patients with poor biological graft survival prognosis, including those with glaucoma or prior surgeries, expanding its potential use and addressing the global donor cornea shortage. Full article

In the evolving landscape of healthcare, telemedicine has emerged as a transformative solution, effectively bridging gaps in medical service delivery across diverse geographic regions. Particularly in rural areas, where access to immediate and specialized care remains limited, store-and-forward telemedicine provides a powerful and practical approach. In rural emergency healthcare settings, resource limitations, specialist shortages, and unreliable connectivity frequently delay critical medical interventions. To address these challenges, this study proposes a store-and-forward telemedicine framework optimized through the use of queueing networks, aiming to enhance emergency response efficiency. The proposed model is structured as a four-node system comprising initial registration, consultation, diagnosis, and treatment. Each node operates as a service queue where patient data are sequentially collected, prioritized, and forwarded. By employing an open queueing network structure, the model devises steady-state probabilities for the number of patients at each node, facilitating a detailed performance analysis of patient flows. Symmetry plays a critical role in maintaining patient flow balance and system stability within the store-and-forward telemedicine model. When the routing probabilities between nodes are balanced, the queueing network exhibits probabilistic symmetry, ensuring consistent transition behavior. Moreover, the directed graph representation of the system demonstrates structural symmetry, reflecting identical service times at all nodes and uniform transition probabilities between nodes. Incorporating the concept of symmetry enables a simplified analytical approach, reduces computational complexity, and provides a more accurate approximation model for evaluating system performance. Full article

Background: During the past years, brain drain has become an international problem. A shortage of human resources in the medical field revealed its challenges during the COVID-19 pandemic. Despite the large number of medical school graduates in Egypt, the reports of the World Health Organization and the Egyptian Medical Syndicate indicate a yearly decline in the number of physicians in Egypt. The aim of this study is to determine the factors affecting Egyptian physicians’ intention to leave Egypt to work in another country. Methods: This is a cross-sectional study with a self-administered questionnaire via the snowball sampling technique that focused on Egyptian physicians who graduated from Egyptian universities and were working in Egypt. The outcome measures include the migration intentions of physicians and their satisfaction with different aspects of work, their family and housing conditions, and their demographic characteristics. Results: The questionnaire was answered by 249 Egyptian physicians, 140 of whom were living in Egypt. The findings revealed that 66.4% of the sample in Egypt were considering working abroad. Work satisfaction was the main factor affecting the decisions of physicians in Egypt; the physicians with a lower satisfaction score were 20 times more likely to leave their country. Additionally, physicians who classified themselves as having a low economic status, being younger, or who were male were more likely to leave. Conclusion: Egypt may suffer major resource losses as a result of the high degree of dissatisfaction among physicians regarding their working conditions. To realize Egypt’s Vision 2030 for health and economic progress, policymakers need to address the driving factors and take probable intervention measures to reduce the drain of our physicians. Full article

Semiconductors underpin modern technology, enabling applications from power electronics and photovoltaics to communications and medical diagnostics. However, the industry faces pressing challenges, including shortages of critical raw materials and the unsustainable nature of conventional fabrication processes. Recent developments in quantum computing and topological quantum materials offer a transformative path forward. In particular, materials exhibiting non-Hermitian physics and topological protection, such as topological insulators and superconductors, enable robust, energy-efficient electronic states. These states are resilient to disorder and local perturbations, positioning them as ideal candidates for next-generation quantum devices. Non-Hermitian systems, which break traditional Hermitian constraints, have revealed phenomena like the skin effect, wherein eigenstates accumulate at boundaries, violating bulk-boundary correspondence. This effect has recently been observed in semiconductor-based quantum Hall devices, marking a significant milestone in condensed matter physics. By integrating these non-Hermitian topological principles into semiconductor technology, researchers can unlock new functionalities for fault-tolerant quantum computing, low-power electronics, and ultra-sensitive sensing platforms. This convergence of topology, quantum physics, and semiconductor engineering may redefine the future of electronic and photonic devices. Full article

Background/Objectives: This study identifies barriers to maternal and child healthcare access in Afghanistan under the Taliban and proposes solutions using the WHO Health System Building Blocks Framework. Methods: Midwives and mothers were recruited via chain-referral sampling. After obtaining IRB and the participants’ informed consent, in-depth virtual interviews, guided by Social Cognitive Theory, were recorded, transcribed, and analyzed using content analysis in MAXQDA 2020. Results: Data analysis revealed four primary consequences of the political unrest in Afghanistan that have exacerbated barriers to accessing maternal and child healthcare: (a) Taliban-imposed restrictions on women’s education and mobility, reducing female healthcare providers and limiting mothers’ access to care; (b) increased poverty, preventing women from attending perinatal visits due to out-of-pocket costs; (c) the deterioration of healthcare services, including medicine shortages, weakened health financing due to donor withdrawals, lack of insurance, and poor governance; and (d) the increased perpetuation of misinformation and harmful practices, such as the use of clergymen for medical advice instead of doctors. Using the WHO Health Systems Framework, we recommend solutions that address issues in service delivery, health workforce, health information systems, access to essential medicines, financing, and governance. Conclusions: This is the first qualitative study capturing Afghan mothers’ and healthcare providers’ experiences under Taliban rule. Our findings can inform international efforts to advocate for women’s healthcare and education rights and guide global aid programs in strengthening Afghanistan’s healthcare system in alignment with Sustainable Development Goal 5. Full article

Wrist fractures, especially those involving the elbow and distal radius, are the most common injuries in children, teenagers, and young adults, with the highest occurrence rates during adolescence. However, the demand for medical imaging and the shortage of radiologists make it challenging to ensure accurate diagnosis and treatment. This study explores how AI-driven approaches are used to enhance fracture detection and improve diagnostic accuracy. In this paper, we propose the latest version of YOLO (i.e., YOLO11) with an attention module, designed to refine detection correctness. We integrated attention mechanisms, such as Global Attention Mechanism (GAM), channel attention, and spatial attention with Residual Network (ResNet), to enhance feature extraction. Moreover, we developed the ResNet_GAM model, which combines ResNet with GAM to improve feature learning and model performance. In this paper, we apply a data augmentation process to the publicly available GRAZPEDWRI-DX dataset, which is widely used for detecting radial bone fractures in X-ray images of children. Experimental findings indicate that integrating Squeeze-and-Excitation (SE_BLOCK) into YOLO11 significantly increases model efficiency. Our experimental results attain state-of-the-art performance, measured by the mean average precision (mAP50). Through extensive experiments, we found that our model achieved the highest mAP50 of 0.651. Meanwhile, YOLO11 with GAM and ResNet_GAM attained a maximum precision of 0.799 and a recall of 0.639 across all classes on the given dataset. The potential of these models to improve pediatric wrist imaging is significant, as they offer better detection accuracy while still being computationally efficient. Additionally, to help surgeons identify and diagnose fractures in patient wrist X-ray images, we provide a Fracture Detection Web-based Interface based on the result of the proposed method. This interface reduces the risk of misinterpretation and provides valuable information to assist in making surgical decisions. Full article