Search Results (2,278)

Drawing inspiration from biological brains’ energy-efficient information-processing mechanisms, photonic integrated circuits (PICs) have facilitated the development of ultrafast artificial neural networks. This in turn is envisaged to offer potential solutions to the growing demand for artificial intelligence employing machine learning in various domains, from nonlinear optimization and telecommunication to medical diagnosis. In the meantime, silicon photonics has emerged as a mainstream technology for integrated chip-based applications. However, challenges still need to be addressed in scaling it further for broader applications due to the requirement of co-integration of electronic circuitry for control and calibration. Leveraging physics in algorithms and nanoscale materials holds promise for achieving low-power miniaturized chips capable of real-time inference and learning. Against this backdrop, we present the State of the Art in neuromorphic photonic computing, focusing primarily on architecture, weighting mechanisms, photonic neurons, and training, while giving an overall view of recent advancements, challenges, and prospects. We also emphasize and highlight the need for revolutionary hardware innovations to scale up neuromorphic systems while enhancing energy efficiency and performance. Full article

This paper aims to leverage artificial intelligence (AI) to assist physicians in utilizing advanced deep learning techniques integrated into developed models within electronic health records (EHRs) in medical information systems (MISes), which have been in use for over 15 years in health centers across the Republic of Serbia. This paper presents a human-centered AI approach that emphasizes physician decision-making supported by AI models. This study presents two developed and implemented deep neural network (DNN) models in the EHR. Both models were based on data that were collected during the COVID-19 outbreak. The models were evaluated using five-fold cross-validation. The convolutional neural network (CNN), based on the pre-trained VGG19 architecture for classifying chest X-ray images, was trained on a publicly available smaller dataset containing 196 entries, and achieved an average classification accuracy of 91.83 ± 2.82%. The DNN model for optimizing patient appointment scheduling was trained on a large dataset (341,569 entries) and a rich feature design extracted from the MIS, which is daily used in Serbia, achieving an average classification accuracy of 77.51 ± 0.70%. Both models have consistent performance and good generalization. The architecture of a realized MIS, incorporating the positioning of developed AI tools that encompass both developed models, is also presented in this study. Full article

This study aimed at proposing a predictive model for real-time monitoring of epidemic dynamics at the municipal scale in Lombardy region, in northern Italy, leveraging Emergency Medical Services (EMS) dispatch data and Geographic Information Systems (GIS) methodologies. Unlike traditional epidemiological models that rely on official diagnoses and offer limited spatial granularity, our approach uses EMS call data (rapidly collected, geo-referenced, and unbiased by institutional delays) as an early proxy for outbreak detection. The model integrates spatial filtering and machine learning (random forest classifier) to categorize municipalities into five epidemic scenarios: from no diffusion to active spread with increasing trends. Developed in collaboration with the Lombardy EMS agency (AREU), the system is designed for operational applicability, emphasizing simplicity, speed, and interpretability. Despite the complexity of the phenomenon and the use of a five-class output, the model shows promising predictive capacity, particularly for identifying outbreak-free areas. Performance is affected by changing epidemic dynamics, such as those induced by widespread vaccination, yet remains informative for early warning. The framework supports health decision-makers with timely, localized insights, offering a scalable tool for epidemic preparedness and response. Full article

Objectives: This study aimed to assess the knowledge, attitudes, and self-confidence of physicians and dentists in Croatia regarding the relationship between oral and systemic health, focusing on periodontal disease and oral manifestations of systemic diseases. Methods: A cross-sectional, web-based survey was conducted among physicians and dentists in Croatian primary healthcare. The questionnaire addressed six thematic domains, including demographic information, knowledge, self-assessment, and clinical practice. Descriptive and comparative statistical analyses were performed. Results: A total of 529 respondents were included (291 physicians and 238 dentists). The mean knowledge score for the association between periodontitis and systemic diseases was 6.8 ± 3.6 out of 15, indicating limited knowledge. For oral manifestations of systemic diseases, the mean score was 10.0 ± 3.8 out of 16, reflecting moderate proficiency. Dentists scored higher than physicians in both domains, though not significantly (p > 0.05). Routine oral mucosal examinations were reported by 89.5% of dentists and 43.0% of physicians (p ≤ 0.001). Only 21.3% of physicians correctly identified the link between periodontitis and adverse pregnancy outcomes, compared to 58.8% of dentists. The primary barriers to effective clinical management were a lack of experience (52.7%) and inadequate education. While 68.3% of dentists felt adequately educated on oral–systemic links, only 22.7% of physicians reported the same. Conclusions: Significant gaps in knowledge and confidence were observed, particularly among physicians. These findings underscore the need to integrate oral–systemic health topics into medical education and to promote interprofessional collaboration to improve patient outcomes. Full article

As medical technology develops and digital demands grow, personal health records (PHRs) are becoming more patient-centered than before based on cloud-based health information exchanges. While enhancing data accessibility and sharing, these systems present privacy and security issues, including data breaches and unauthorized access. We developed a post-quantum, group-oriented encryption scheme using the Crystal-Kyber Key encapsulation mechanism (KEM). Leveraging lattice-based post-quantum cryptography, this scheme ensures quantum resilience and chosen ciphertext attack security for layered cloud PHR environments. It supports four encryption modes: individual, group, subgroup-specific, and authorized subgroup decryption, meeting diverse data access needs. With efficient key management requiring only one private key per user, the developed scheme strengthens the privacy and security of PHRs in a future-proof, flexible, and scalable manner. Full article

Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows. Full article

Background/Objectives. This manuscript presents an overview of advances in oncological radiotherapy as an effective treatment method for cancerous tumors, focusing on mechanisms of action within metabolite–antimetabolite systems. The urgency of this topic is underscored by the fact that cancer remains one of the leading causes of death worldwide: as of 2022, approximately 20 million new cases were diagnosed globally, accounting for about 0.25% of the total population. Given prognostic models predicting a steady increase in cancer incidence to 35 million cases by 2050, there is an urgent need for the latest developments in physics, chemistry, molecular biology, pharmacy, and strict adherence to oncological vigilance. The purpose of this work is to demonstrate the relationship between the nature and mechanisms of past diagnostic and therapeutic oncology approaches, their current improvements, and future prospects. Particular emphasis is placed on isotope technologies in the production of therapeutic nuclides, focusing on the mechanisms of formation of simple and complex theranostic compounds and their classification according to target specificity. Methods. The methodology involved searching, selecting, and analyzing information from PubMed, Scopus, and Web of Science databases, as well as from available official online sources over the past 20 years. The search was structured around the structure–mechanism–effect relationship of active pharmaceutical ingredients (APIs). The manuscript, including graphic materials, was prepared using a narrative synthesis method. Results. The results present a sequential analysis of materials related to isotope technology, particularly nucleus stability and instability. An explanation of theranostic principles enabled a detailed description of the action mechanisms of radiopharmaceuticals on various receptors within the metabolite–antimetabolite system using specific drug models. Attention is also given to radioactive nanotheranostics, exemplified by the mechanisms of action of radioactive nanoparticles such as Tc-99m, AuNPs, wwAgNPs, FeNPs, and others. Conclusions. Radiotheranostics, which combines the diagnostic properties of unstable nuclei with therapeutic effects, serves as an effective adjunctive and/or independent method for treating cancer patients. Despite the emergence of resistance to both chemotherapy and radiotherapy, existing nuclide resources provide protection against subsequent tumor metastasis. However, given the unfavorable cancer incidence prognosis over the next 25 years, the development of “preventive” drugs is recommended. Progress in this area will be facilitated by modern medical knowledge and a deeper understanding of ligand–receptor interactions to trigger apoptosis in rapidly proliferating cells. Full article

Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. Methods: We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick’s gray level co-occurrence matrix (GLCM) textural features). Results: Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8–128, while preserving trends. Conclusions: When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images. Full article

Self-medication is a subject of global importance. If practiced responsibly, self-medication represents a part of self-care or positive care of an individual or a community in promoting their own health. However, today’s practices of self-medication are often inappropriate and irresponsible, and as such appear all over the world. Inappropriate self-medication can be connected with possible serious health risks and consequences. Therefore, it represents a global health issue. It can even generate additional health problems, which will eventually become a burden to healthcare systems and can induce significant costs, which also raises socioeconomic concerns. Hence, self-medication attracts the attention of researchers and practitioners globally in efforts to clarify the current status and define feasible measures that should be implemented to address this issue. This narrative review aims to give an overview of the situation in the field of self-medication globally, including current practices and attitudes, as well as implications for actions needed to improve this problem. A PubMed/MEDLINE search was conducted for articles published in the period from 1995 up to March 2025 using keywords “self-medication” or “selfmedication” alone or in combinations with terms related to specific subthemes related to self-medication, such as COVID-19, antimicrobials, healthcare professionals, and storing habits of medicines at home. Studies were included if self-medication was their main focus. Publications that only mentioned self-medication in different contexts, but not as their main focus, were excluded. Considering the outcomes of research on self-medication in various contexts, increasing awareness of responsible self-medication through education and informing, together with surveillance of particular medicines and populations, could lead to more appropriate and beneficial self-medication in the future. Full article

Background: Youth with chronic rheumatologic diseases undergo medical experiences that can lead to post-traumatic stress disorder (PTSD). Understudied in pediatric rheumatology, medical PTSD can be significantly distressing and impairing. Objective: This study explored the prevalence of medical PTSD symptoms in youth with chronic inflammatory arthritis and associated factors, including pain, disease activity, mental health history, and anxiety sensitivity. Methods: A cross-sectional study of 50 youth (ages 8–18) with juvenile idiopathic arthritis (JIA) and childhood-onset systemic lupus erythematous (cSLE) was conducted at a pediatric rheumatology clinic. Participants completed self-report measures assessing post-traumatic stress symptoms (CPSS-V), pain, anxiety sensitivity (CASI), pain-related self-efficacy (CSES), adverse childhood experiences (ACEs), and fibromyalgia symptoms (PSAT). Clinical data included diagnoses, disease activity, treatment history, and demographics. Results: Forty percent had trauma symptoms in the moderate or more severe range. The 14% likely meeting criteria for probable medical PTSD were older (median 17 vs. 15 years, p = 0.005), had higher pain scores (median 4 vs. 3, p = 0.008), more ACEs (median 3 vs. 1, p = 0.005), higher anxiety sensitivity scores (median 39 vs. 29, p = 0.008), and higher JIA disease activity scores (median cJADAS-10 11.5 vs. 7.5, p = 0.032). They were also more likely to report a history of depression (71 vs. 23%, p = 0.020). No associations were found with hospitalization or injected/IV medication use. Conclusions: Medical trauma symptoms are prevalent in youth with chronic inflammatory arthritis. Probable PTSD was associated with pain and psychological distress. These findings support the need for trauma-informed care in pediatric rheumatology. Full article

Background/Objectives: Respectful maternity care involves privacy, dignity, and informed choice within the process of delivery as stipulated by the World Health Organization (WHO). Informed consent is a cornerstone of patient-centered care, representing not just a formal document, but an ongoing ethical and clinical process through which women are offered objective, understandable information to support autonomous, informed decision-making. Methods: This narrative review critically examines the literature on informed consent in maternity care, with particular attention to both obstetric-led and midwifery-led models of care. In addition to identifying institutional, cultural, and systemic obstacles to its successful implementation, the review examines the definition and application of informed consent in perinatal settings and evaluates its effects on women’s autonomy and satisfaction with care. Results: Important conclusions emphasize that improving women’s experiences and minimizing needless interventions require active decision-making participation, a positive provider–patient relationship, and ongoing support from medical professionals. However, significant gaps persist between legal mandates and actual practice due to provider attitudes, systemic constraints, and sociocultural influences. Women’s experiences of consent can be more effectively understood through the use of instruments such as the Mothers’ Respect (MOR) Index and the Mothers’ Autonomy in Decision Making (MADM) Scale. Conclusions: To promote genuinely informed and considerate maternity care, this review emphasizes the necessity of legislative reform and improved provider education in order to close the gap between policy and practice. Full article

Healthcare Information Systems (HISs) are essential for improving care quality, managing chronic diseases, and supporting clinical decision-making. Despite significant investments, HIS implementations often fail due to the complexity of healthcare environments. Maturity Models (MMs) have emerged as tools to guide organizational improvement by assessing readiness, process efficiency, technology adoption, and interoperability. This study presents a comprehensive literature review identifying 45 Maturity Models used across various healthcare domains, including telemedicine, analytics, business intelligence, and electronic medical records. These models, often based on Capability Maturity Model Integration (CMMI), vary in structure, scope, and maturity stages. The findings demonstrate that structured maturity assessments help healthcare organizations plan, implement, and optimize HIS more effectively, leading to enhanced clinical and operational performance. This review contributes to an understanding of how different MMs can support healthcare digital transformation and provides a resource for selecting appropriate models based on specific organizational goals and technological contexts. Full article

The global rise in urban-related health issues poses significant challenges to public health, particularly in cities facing socio-economic crises. In Lebanon, 70% of the population is experiencing financial hardship, and healthcare costs have surged by 172%, exacerbating the strain on medical services. Given these conditions, improving the quality and accessibility of green spaces offers a promising avenue for alleviating mental health issues in urban areas. This study investigates the psychological impact of nine urban public spaces in Beirut through a comprehensive survey methodology, involving 297 participants (locals and tourists) who rated these spaces using Likert-scale measures. The findings reveal location-specific barriers, with Saanayeh Park rated highest in quality and Martyr’s Square rated lowest. The analysis identifies facility quality as the most significant factor influencing space quality, contributing 73.6% to the overall assessment, while activity factors have a lesser impact. The study further highlights a moderate positive association (Spearman’s rho = 0.30) between public space quality and mental well-being in Beirut. This study employs a hybrid methodology combining Research for Design (RfD) and Research Through Designing (RTD). Empirical data informed spatial strategies, while iterative design served as a tool for generating context-specific knowledge. Design enhancements—such as sensory plantings, shading systems, and social nodes—aim to improve well-being through better public space quality. The proposed interventions support mental health, life satisfaction, climate resilience, and urban inclusivity. The findings offer actionable insights for cities facing public health and spatial equity challenges in crisis contexts. Full article

Introduction: Adolescents and young adults (AYAs), especially those from underserved communities, often face barriers to sexual and reproductive health (SRH). This pilot study evaluated the implementation of mobile health technologies to promote SRH care, including the integration of the Rapid Adolescent Prevention Screening^TM (RAAPS) and the Health-E You/Salud iTu^TM (Health-E You) app at a School-Based Health Center (SBHC) in Los Angeles using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework. Methods: This multi-method pilot study included the implementation of an integrated tool with two components, the RAAPS electronic health screening tool and the Health-E You app, which delivers tailored SRH education and contraceptive decision support to patients (who were sex-assigned as female at birth) and provides an electronic summary to clinicians to better prepare them for the visit with their patient. Quantitative data on tool usage were collected directly from the back-end data storage for the apps, and qualitative data were obtained through semi-structured interviews and in-clinic observations. Thematic analysis was conducted to identify implementation barriers and facilitators. Results: Between April 2024 and June 2024, 60 unique patients (14–19 years of age) had a healthcare visit. Of these, 35.00% used the integrated RAAPS/Health-E You app, and 88.33% completed the Health-E You app only. All five clinic staff were interviewed and expressed that they valued the tools for their educational impact, noting that they enhanced SRH discussions and helped uncover sensitive information that students might not disclose face-to-face. However, the tools affected clinic workflows and caused rooming delays due to the time-intensive setup process and lack of integration with the clinic’s primary electronic medical record system. In addition, they also reported that the time to complete the screener and app within the context of a 30-min appointment limited the time available for direct patient care. Additionally, staff reported that some students struggled with the two-step process and did not complete all components of the tool. Despite these challenges, clinic staff strongly supported renewing the RAAPS license and continued use of the Health-E You app, emphasizing the platform’s potential for improving SRH care and its educational value. Conclusions: The integrated RAAPS and Health-E You app platform demonstrated educational value and improved SRH care but faced operational and technical barriers in implementing the tool. These findings emphasize the potential of such tools to address SRH disparities among vulnerable AYAs while providing a framework for future implementations in SBHCs. Full article

Background: Antinuclear antibodies (ANAs) serve as crucial biomarkers for diagnosing systemic autoimmune diseases; however, their interpretation can be complex and may not always correlate with clinical symptoms. Methods: A comprehensive narrative review was conducted to evaluate the peer-reviewed literature published between 1961 and 2025. Databases, including PubMed and Scopus, were searched using combinations of controlled vocabulary and free-text terms relating to antinuclear antibodies and their clinical significance. The objective was to gather and synthesize information regarding the diagnostic utility and interpretation of ANA testing in routine medical practice. Discussion: The indirect immunofluorescence assay (IIF) on HEp-2 cells is established as the gold standard for detecting ANAs, facilitating the classification of various fluorescent patterns. While a positive ANA test can suggest autoimmune disorders, the presence and titre must be interpreted alongside clinical findings, as low titres often lack diagnostic significance. Findings indicate that titres higher than 1:160 may provide greater specificity in differentiating true positives from false positives in healthy individuals. The study also emphasizes the relevance of fluorescence patterns, with specific patterns linked to particular diseases, although many do not have strong clinical correlations. Moreover, certain autoantibodies demonstrate high specificity for diseases like systemic lupus erythematosus (SLE) and mixed connective tissue disease (MCTD). Ultimately, while ANA testing is invaluable for diagnosing connective tissue diseases, healthcare providers must consider its limitations to avoid misdiagnosis and unnecessary treatment. Conclusions: ANA testing is a valuable tool in the diagnosis of connective tissue diseases, but its interpretation must be approached with caution. Clinical context remains crucial when evaluating ANA results to avoid misdiagnosis and overtreatment. This review is about the diagnostic aspects and clinical consequences of ANA testing, as well as highlighting both the diagnostic benefits and the potential limitations of this procedure in everyday clinical practice. The review fills a gap in the literature by integrating the diagnostic and clinical aspects of ANA testing, with a focus on real-world interpretation challenges. Full article