Search Results (1,573)

Background: Cognitive and behavioral interventions have risen in popularity both as an adjunctive treatment to antipsychotic medication and as an alternative treatment for schizophrenia. With the growing number of such interventions, we performed an umbrella review to provide a comprehensive summary comparing the effectiveness of the different interventions among populations with schizophrenia. Methods: This umbrella review included meta-analyses evaluating cognitive and behavioral interventions for schizophrenia. Following PRISMA guidelines, the initial search yielded 4888 records, and after a three-stage screening procedure, 33 meta-analyses met the inclusion criteria for the final analysis. Results: Our findings from the 33 meta-analyses support the efficacy of cognitive and behavioral interventions in reducing total symptoms (Median g = −0.38; Range g = −1.56 to −0.08), positive symptoms (Median g = −0.30; Range g = −0.84 to 0.00), and negative symptoms (Median g = −0.39; Range g = −0.66 to −0.09) of schizophrenia. Cognitive Behavioral Therapy, being the most common intervention studied, exhibited small to medium effects on total and positive symptom alleviation. In addition, there is evidence supporting the effectiveness of family psychoeducation combined with patient behavioral and skills training, exercise therapy, horticultural therapy, and music therapy. Conclusions: While our umbrella review solidifies the current evidence supporting cognitive and behavioral interventions as effective treatments for schizophrenia, it also reveals that treatment efficacy is highly dependent on the type of intervention used. Full article

The expansion of electronic health records (EHRs) has generated a large amount of unstructured textual data, such as clinical notes and medical reports, which contain diagnostic and prognostic information. Effective classification of these textual medical notes is critical for improving clinical decision support and healthcare data management. This study presents a statistically rigorous comparative analysis of four traditional machine learning algorithms—Random Forest, Logistic Regression, Multinomial Naive Bayes, and Support Vector Machine—for multiclass classification of medical notes into four disease categories: Neoplasms, Digestive System Diseases, Nervous System Diseases, and Cardiovascular Diseases. A dataset containing 9633 labeled medical notes was preprocessed through text cleaning, lemmatization, stop-word removal, and vectorization using term frequency-inverse document frequency (TF–IDF) representation. The models were trained and optimized through GridSearchCV with 5-fold cross-validation and evaluated across five independent stratified 90-10 train–test splits. Evaluation metrics, including accuracy, precision, recall, F1-score, and multiclass ROC-AUC, were used to assess model performance. Logistic Regression demonstrated the strongest overall performance, achieving an average accuracy of 0.8469 and high macro and weighted F1 scores, followed by Support Vector Machine and Multinomial Naive Bayes. Misclassification patterns revealed substantial lexical overlap between digestive and neurological disease notes, underscoring the limitations of TF–IDF representations in capturing deeper semantic distinctions. These findings confirm that traditional machine learning models remain robust, interpretable, and computationally efficient tools for textual medical note classification, and the study establishes a transparent and reproducible benchmark that provides a solid foundation for future methodological advancements in clinical natural language processing. Full article

Digital twins (DTs) powered by artificial intelligence (AI) are becoming important transformational tools in healthcare, enabling real-time simulation and personalized decision support at the patient level. The aim of this review is to critically examine the evolution, current applications, and future potential of AI-based DTs in healthcare, with a particular focus on their role in enabling real-time simulation and personalized patient-level decision support. Specifically, the review aims to provide a comprehensive overview of how AI-based DTs are being developed and implemented in various clinical domains, identifying existing scientific and technical gaps and highlighting methodological, regulatory, and ethical issues. Taking a “local to global” perspective, the review aims to explore how individual patient-level models can be scaled and integrated to inform population health strategies, global data networks, and collaborative research ecosystems. This will provide a structured foundation for future research, clinical applications, and policy development in this rapidly evolving field. Locally, DTs allow medical professionals to model individual patient physiology, predict disease progression, and optimize treatment strategies. Hospitals are implementing AI-based DT platforms to simulate workflows, efficiently allocate resources, and improve patient safety. Generative AI further enhances these applications by creating synthetic patient data for training, filling gaps in incomplete records, and enabling privacy-respecting research. On a broader scale, regional health systems can use connected DTs to model population health trends and predict responses to public health interventions. On a national scale, governments and policymakers can use these insights for strategic planning, resource allocation, and increasing resilience to health crises. Internationally and globally, AI-based DTs can integrate diverse datasets across borders to support research collaboration and improve early pandemic detection. Generative AI contributes to global efforts by harmonizing heterogeneous data, creating standardized virtual patient cohorts, and supporting cross-cultural medical education. Combining local precision with global insights highlights DTs’ role as a bridge between personalized and global health. Despite the efforts of medical and technical specialists, ethical, regulatory, and data governance challenges remain crucial to ensuring responsible and equitable implementation worldwide. In conclusion, AI-based DTs represent a transformative paradigm, combining individual patient care with systemic and global health management. These perspectives highlight the potential of AI-based DTs to bridge precision medicine and public health, provided ethical, regulatory, and governance challenges are addressed responsibly. Full article

Background: The rising frequency and complexity of disasters underscores the urgent need for robust preparedness in healthcare. Nurses and nursing students, as key frontline responders, often lack sufficient training to respond effectively to emergencies and recovery efforts. Aim: This review evaluates the effectiveness of disaster preparedness training in terms of nurses’ and nursing students’ self-efficacy in providing disaster care and determines which training approaches are most effective. Method: A systematic review was conducted of peer-reviewed articles published in English between 2014 and 2025 across Medline, PubMed, ProQuest, and Health & Medical Col. Search terms included nurses, nursing students, self-efficacy, disaster training, emergency preparedness, training, simulation and scenario-based learning. Results: Nineteen peer-reviewed studies met the inclusion criteria. Overall, disaster preparedness training was found to enhance nurses’ and nursing students’ self-efficacy, knowledge and skills, with simulation-based and scenario-driven approaches producing the most consistent gains. These methods provided realistic and immersive experiences that fostered confidence and strengthened preparedness. Traditional lectures and workshops also improved outcomes but were generally less effective in sustaining self-efficacy over time. Reported challenges included limited faculty expertise, insufficient institutional support, and psychological barriers that may reduce engagement and impact. Conclusion: Integrating disaster preparedness into nursing curricula and professional training is vital for strengthening nurses’ and nursing students’ self-efficacy in crisis response. Evidence shows that simulation-based education, particularly when combined with traditional approaches, is especially effective in building knowledge and skills. Embedding these methods into training frameworks offers a sustainable strategy to ensure a more competent and resilient nursing workforce. Full article

Online adaptive radiotherapy (ART) is rapidly transforming clinical radiation oncology by enabling adaptation of treatment plans based on patient-specific anatomical and biological changes. However, most medical physics training programs lack structured education in ART. To address this critical gap, the Medical Physics Adaptive Radiotherapy (MPART) Fellowship was established at our center to train post-residency or practicing physicists in advanced adaptive technologies and workflows. The MPART Fellowship is a two-year program that provides immersive, platform-specific training in CBCT-guided (Varian Ethos), MR-guided (Elekta Unity), and PET-guided (RefleXion X1) radiotherapy. Fellows undergo modular clinical rotations, hands-on training, and dedicated research projects. The curriculum incorporates competencies in imaging, contouring, online planning, quality assurance, and team-based decision-making. Evaluation is based on the Accreditation Council for Graduate Medical Education competency domains and includes milestone tracking, mentor reviews, and structured presentations. The fellowship attracted applicants from both domestic and international institutions, reflecting strong demand for formal ART training. Out of 22 applications, two fellows have been successfully recruited into the program since 2024. Fellows actively participate in all phases of adaptive workflows and are expected to function at near-attending levels by the second year of their training. Each fellow also leads at least one translational or operational research project aimed at improving ART delivery. Fellows contribute to clinical coverage and lead developmental projects, resulting in presentations and publications at the national and international levels. The MPART Fellowship addresses a vital educational need by equipping medical physicists with the advanced competencies necessary for implementing and leading ART. This program offers a replicable framework for other institutions seeking to advance precision radiation therapy through structured post-residency training in adaptive radiotherapy. As this fellowship program is still in its early phase of establishment, the primary goal of this paper is to introduce the structure, framework, and implementation model of the program. Comprehensive outcome analyses—such as quantitative assessments, fellow feedback, and longitudinal competency evaluations—will be incorporated in future work as additional cohorts complete training. Full article

Background/Objectives: Acute respiratory distress syndrome (ARDS) represents a critical complication in polytrauma patients, characterized by diffuse lung inflammation and bilateral pulmonary infiltrates with mortality rates reaching 45% in intensive care units (ICU). The heterogeneous nature of ARDS and complex clinical presentation in severely injured patients poses substantial diagnostic challenges, necessitating early prediction tools to guide timely interventions. Machine learning (ML) algorithms have emerged as promising approaches for clinical decision support, demonstrating superior performance compared to traditional scoring systems in capturing complex patterns within high-dimensional medical data. Based on the identified research gaps in early ARDS prediction for polytrauma populations, our study aimed to: (i) develop a balanced random forest (BRF) ML model for early ARDS prediction in critically ill polytrauma patients, (ii) identify the most predictive clinical features using ANOVA-based feature selection, and (iii) evaluate model performance using comprehensive metrics addressing class imbalance challenges. Methods: This retrospective cohort study analyzed 407 polytrauma patients admitted to the ICU of the Center of Traumatology and Major Burns of Ben Arous, Tunisia, between 2017 and 2021. We implemented a comprehensive ML pipeline that incorporates Tomek Links undersampling, ANOVA F-test feature selection for the top 10 predictive variables, and SMOTE oversampling with a conservative sampling rate of 0.3. The BRF classifier was trained with class weighting and evaluated using stratified 5-fold cross-validation. Performance metrics included AUROC, PR-AUC, sensitivity, specificity, F1-score, and Matthews correlation coefficient. Results: Among 407 patients, 43 developed ARDS according to the Berlin definition, representing a 10.57% incidence. The BRF model demonstrated exceptional predictive performance with an AUROC of 0.98, a sensitivity of 0.91, a specificity of 0.80, an F1-score of 0.84, and an MCC of 0.70. Precision–recall AUC reached 0.86, demonstrating robust performance despite class imbalance. During stratified cross-validation, AUROC values ranged from 0.93 to 0.99 across folds, indicating consistent model stability. The top 10 selected features included procalcitonin, PaO₂ at ICU admission, 24-h pH, massive transfusion, total fluid resuscitation, presence of pneumothorax, alveolar hemorrhage, pulmonary contusion, hemothorax, and flail chest injury. Conclusions: Our BRF model provides a robust, clinically applicable tool for early prediction of ARDS in polytrauma patients using readily available clinical parameters. The comprehensive two-step resampling approach, combined with ANOVA-based feature selection, successfully addressed class imbalance while maintaining high predictive accuracy. These findings support integrating ML approaches into critical care decision-making to improve patient outcomes and resource allocation. External validation in diverse populations remains essential for confirming generalizability and clinical implementation. Full article

Minimally invasive facial procedures are widely performed in clinical medicine but remain associated with severe complications such as necrosis or blindness, often resulting from insufficient anatomical understanding and limited procedural training. To address these challenges, this study developed an anatomically accurate clinical simulator for facial injection training. A three-dimensional polygonal facial model was constructed using standardized anatomical datasets reflecting skeletal dimensions, soft tissue characteristics and the average arterial distribution of East Asian faces. This model was integrated into simulation software connected to a facial silicone dummy with realistic tissue texture and an optical tracking system providing sub-millimeter precision. Each anatomical structure, including muscles, vessels and nerves, was digitally annotated and linked to interactive visualization tools. During training, the simulator simultaneously reflected the real-time needle trajectory and insertion depth; when the needle tip approached a high-risk structure, such as the supraorbital artery, alerts were automatically triggered. This feedback enabled trainees to recognize unsafe injection zones and adjust their technique accordingly. The system provided a realistic, repeatable and safe environment for improving anatomical comprehension and procedural accuracy. This study proposes an innovative applied simulation system that may enhance medical education and clinical safety in facial injection procedures. Full article

Background: Aneurysms are life-threatening vascular conditions that require early and accurate detection to prevent fatal outcomes. Methods: Advances in deep learning have demonstrated significant potential in medical image analysis, particularly for automated aneurysm detection. This review paper provides an overview of selected deep-learning approaches for aneurysm detection, with an emphasis on YOLO (You Only Look Once) models and their architectural characteristics, comparative performance, and applicability. Results: Existing YOLO-based studies are examined and compared, highlighting their strengths, limitations, dataset usage, performance metrics, and clinical relevance. To complement this overview, we include test using an annotated dataset of 1342 angiograms. Of these, 1074 angiograms were used for model training and 10% (approximately 107 angiograms) were reserved for validation; the remaining 268 were used for evaluation, with all annotations provided by a radiology specialist. Conclusions: These tests were conducted solely to provide practical examples and a limited comparative demonstration of the selected YOLO variants, rather than to constitute a full original experimental study. The findings underscore the potential of integrating detection models to improve accuracy and robustness in aneurysm identification, paving the way for more reliable and validated computer-aided diagnostic systems. Full article

At present, there are some devices for muscle strength training after knee surgery, such as elastic bands and isokinetic muscle strength training instruments, but most of them are expensive or cannot monitor training progress. Triboelectric nanogenerators (TENGs) have proven to be reliable self-sensing devices. There have been some applications in the field of rehabilitation, but few have been used for muscle strength training. Our team has innovatively applied the TENG self-sensing device to the self-rehabilitation management of the knee joint post-surgery. We have developed the “Triboelectric Nanogenerator for Muscle Strength Training of Knee Joint after Surgery” (MSTKJS-TENG), which is significantly more integrated than traditional instruments (volume: 120 mm × 100 mm × 100 mm) and can real-time track the number and quality of movements completed by patients during muscle strength training. The development of this device has made up for the deficiencies of traditional instruments. It can assist medical staff in remotely evaluating the recovery of patients’ postoperative muscle strength to a certain extent, thereby adjusting training intensity in a timely manner and providing personalized guidance. Meanwhile, the research on this device provides effective technical support and innovation for the development of smart rehabilitation medicine. Full article

Introduction: The scope and accessibility of prenatal testing have significantly expanded in recent years, reaching a broader population of pregnant women. Advances in non-invasive diagnostic methods support informed decision-making and help reduce the need for invasive procedures. Objective: The objective was to evaluate pregnant women’s knowledge regarding prenatal testing and assess the quality of information provided by healthcare professionals, including the frequency of screening and invasive procedures. Materials and Methods: A total of 310 obstetric patients from maternity wards in two hospitals in northern Poland completed a survey addressing prenatal tests, sources of information, and the quality of guidance received from medical staff. Results: Nearly 75% of respondents demonstrated adequate knowledge of the purpose, indications, and scope of prenatal testing. Physicians were identified as the primary source of information. Approximately 50% correctly indicated the recommended number of ultrasound examinations during pregnancy. No correlation was observed between knowledge of prenatal testing and a history of delivering a child with health complications. The combined first-trimester test was performed in 48.6% of cases, NIPT in 11.6%, and invasive testing in 1.8% of the study group. Conclusions: Public awareness of prenatal testing in Poland remains insufficient. With the introduction of partially reimbursed tests in 2024, we recommend strengthening educational efforts through social campaigns and targeted training for healthcare professionals. Full article

Purpose: While hybrid quantum–classical neural networks (HNNs) are a promising avenue for quantum advantage, the critical influence of the classical backbone architecture on their performance remains poorly understood. This study investigates the role of lightweight convolutional neural network architectures, focusing on LCNet, in determining the stability, generalization, and effectiveness of hybrid models augmented with quantum layers for medical applications. The objective is to clarify the architectural compatibility between quantum and classical components and provide guidelines for backbone selection in hybrid designs. Methods: We constructed HNNs by integrating a four-qubit quantum circuit (with trainable rotations) into scaled versions of LCNet (050, 075, 100, 150, 200). These models were rigorously evaluated on CIFAR-10 and MedMNIST using stratified 5-fold cross-validation, assessing accuracy, AUC, and robustness metrics. Performance was assessed with accuracy, macro- and micro-averaged area under the ROC curve (AUC), per-class accuracy, and out-of-fold (OoF) predictions to ensure unbiased generalization. In addition, training dynamics, confusion matrices, and performance stability across folds were analyzed to capture both predictive accuracy and robustness. Results: The experiments revealed a strong dependence of hybrid network performance on both backbone architecture and model scale. Across all tests, LCNet-based hybrids achieved the most consistent benefits, particularly at compact and medium configurations. From LCNet050 to LCNet100, hybrid models maintained high macro-AUC values exceeding 0.95 and delivered higher mean accuracies with lower variance across folds, confirming enhanced stability and generalization through quantum integration. On the DermaMNIST dataset, these hybrids achieved accuracy gains of up to seven percentage points and improved AUC by more than three points, demonstrating their robustness in imbalanced medical settings. However, as backbone complexity increased (LCNet150 and LCNet200), the classical architectures regained superiority, indicating that the advantages of quantum layers diminish with scale. The mostconsistent gains were observed at smaller and medium LCNet scales, where hybridization improved accuracy and stability across folds. This divergence indicates that hybrid networks do not necessarily follow the “bigger is better” paradigm of classical deep learning. Per-class analysis further showed that hybrids improved recognition in challenging categories, narrowing the gap between easy and difficult classes. Conclusions: The study demonstrates that the performance and stability of hybrid quantum–classical neural networks are fundamentally determined by the characteristics of their classical backbones. Across extensive experiments on CIFAR-10 and DermaMNIST, LCNet-based hybrids consistently outperformed or matched their classical counterparts at smaller and medium scales, achieving higher accuracy and AUC along with notably reduced variability across folds. These improvements highlight the role of quantum layers as implicit regularizers that enhance learning stability and generalization—particularly in data-limited or imbalanced medical settings. However, the observed benefits diminished with increasing backbone complexity, as larger classical models regained superiority in both accuracy and convergence reliability. This indicates that hybrid architectures do not follow the conventional “larger-is-better” paradigm of classical deep learning. Overall, the results establish that architectural compatibility and model scale are decisive factors for effective quantum–classical integration. Lightweight backbones such as LCNet offer a robust foundation for realizing the advantages of hybridization in practical, resource-constrained medical applications, paving the way for future studies on scalable, hardware-efficient, and clinically reliable hybrid neural networks. Full article

Introduction: Cancer pain remains a critical issue for patients’ quality of life, affecting their physiology, psychology, and social relationships. Despite the widely recognized role of pharmacists in pain management, their involvement in palliative care in Greece remains limited. This study focuses on exploring the perceptions and experiences of pharmacists regarding their role in cancer pain management, identifying barriers, required skills, and proposing strategies for their integration in the multidisciplinary team. Μaterials and Μethods: Qualitative research was conducted through semi-structured interviews with seven pharmacists in the Attica region. The interviews were recorded, transcribed, and thematically analyzed. Results: The analysis revealed four main themes: (1) limited access to medical records and challenges in pharmaceutical decision-making, (2) lack of institutional frameworks and a culture of collaboration, (3) need for specialized education and continuous training, and (4) understaffing and bureaucracy, faced by pharmacists. Discussion: This study highlights the underutilized role of pharmacists in cancer pain management in Greece. Barriers such as restricted access to patient records, weak interdisciplinary collaboration, insufficient training, and bureaucratic constraints limit their contribution. Structured frameworks and collaborative cultures can enhance pharmacists’ involvement, while education and continuous training are essential to strengthen their legitimacy within care teams. Digital tools can improve access to patient information and support evidence-based decisions. Conclusions: Pharmacists’ integration in the patient’s management team has significant benefits for the patient’s quality of life. Strengthening pharmacists’ involvement in cancer pain management requires the establishment of collaborations, continuous education, bureaucratic simplification, and the integration of digital tools. The development of practical resources, such as educational guides, can play a pivotal role in enhancing the quality of care provided. Full article

Food supplements (FSs) are widely used by the general population and are commonly available in community pharmacies. As highly accessible healthcare professionals, pharmacists are well positioned to provide evidence-based information and guidance regarding their safe and appropriate use. Adequate knowledge of FSs is essential for pharmacists to prevent adverse effects, identify potential interactions with other medications, and ensure rational use. The objective of this study was to assess community pharmacists’ knowledge regarding FSs and their attitudes towards dispensing and patient counseling practices. A literature review was carried out using the scientific databases PubMed, Scopus, and Web of Science. The following keywords were used: (“food supplements” OR “dietary supplements”) AND (“pharmacists’ knowledge”) AND (“pharmacists’ attitudes”). A total of 789 articles were identified from the electronic databases, of which 31 met the inclusion criteria. The majority of studies were conducted in Asia, with fewer in Europe, North America, and Australia. Cross-sectional survey-based studies represented the predominant research design. The analyzed studies showed that community pharmacists generally demonstrate insufficient knowledge regarding FSs. Nonetheless, they tend to hold a positive attitude toward the use of FSs and recognize their responsibility to counsel patients on safe consumption. Several barriers affecting pharmacists’ ability to deliver evidence-based guidance were identified, including limited training, lack of basic nutrition education, and insufficient access to reliable information sources. The findings indicate the need for targeted strategies to enhance pharmacists’ competencies and improve the quality of patient counseling in this domain. Full article

The convergence of wearable technologies and digital twin (DT) systems is transforming rehabilitation engineering, enabling continuous monitoring, personalized therapeutic interventions, and predictive modeling of patient recovery pathways. This review examines the growing role of machine learning (ML) in the development and integration of DTs frameworks in rehabilitation, with a focus on wearable sensor data, security and privacy, edge computing architectures, federated learning paradigms, and generative artificial intelligence (GenAI) applications. We first analyze data collection processes, emphasizing multimodal sensing, signal processing, and real-time synchronization between physical and virtual patient models. We then discuss key challenges related to data security, encryption, and privacy protection, especially in distributed clinical environments. The review then assesses the role of edge computing in reducing latency, improving energy efficiency, and enabling real-time local intelligence feedback in wearable devices. Federated learning approaches are discussed as promising strategies for jointly training ML models without compromising sensitive medical data. Finally, we present new GenAI techniques for generating synthetic data, personalizing digital twins, and simulating rehabilitation scenarios. By mapping current progress and identifying research gaps, this article provides a unified view that connects electronic and biomedical engineering with intelligent, secure, and adaptive DT ecosystems for next-generation rehabilitation solutions. Wearable devices with ML and DTs for rehabilitation are developing rapidly, but their current effectiveness still depends on consistent, high-quality data streams and robust clinical validation. The most promising convergence involves combining edge intelligence with federated learning to enable real-time personalization while preserving patient privacy. GenAI further enhances these systems by simulating patient-specific scenarios, accelerating model adaptation, and treatment planning. Key challenges remain related to standardizing data formats, ensuring comprehensive security, and seamlessly integrating these technologies into clinical processes. Full article

This study investigates how the learning order between segmentation and classification tasks influences performance and generalization in medical image analysis. We propose a Sequential Swin Transformer framework that reuses a shared Transformer backbone with alternating task-specific heads to compare two sequential strategies: (i) segmentation followed by classification and (ii) classification followed by segmentation. Unlike conventional multitask or preprocessing-based pipelines, the proposed framework isolates the impact of task ordering on feature transfer under an identical architecture. Evaluated on the HAM10000 skin lesion dataset, the segmentation-then-classification configuration achieves the highest multiclass accuracy (up to 86.9%) while maintaining strong segmentation performance (Jaccard index ≈ 86%). Statistical tests confirm its superiority in accuracy and macro F1 score, whereas Grad-CAM and t-distributed stochastic neighbor embedding (t-SNE) analyses reveal that segmentation-first training yields more lesion-centered attention and a more discriminative latent space. Cross-domain evaluation on gastrointestinal endoscopy images further demonstrates robust segmentation (Jaccard index ≈ 91%) and multiclass accuracy (≈94.5%), confirming the generalizability of the sequential paradigm. Overall, the proposed method provides a theoretically grounded, clinically interpretable, and reproducible alternative to joint multitask learning approaches, enhancing feature transfer and generalization in medical imaging. Full article