Background: Rare diseases affect more than 300 million people globally, and only about 5% have approved therapies. Lysosomal storage disorders (LSDs) exemplify the diagnostic and long-term care complexity typical of rare diseases, and digital health technologies (DHTs), especially artificial intelligence (AI) and connected care (CC), are emerging tools to support LSD management. Objective: We aimed […]
Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis
Background: People with stroke face a high mortality risk, and an accurate prediction model is essential to the guidance of clinical decision-making in this population. Recently, with growing attention paid to machine learning (ML) in stroke care, some researchers have investigated the effectiveness of ML in predicting the mortality risk in stroke. However, systematic evidence […]
Psychotherapists’ Trust, Distrust, and Generative AI Practices in Psychotherapy: Qualitative Study
Background: Generative artificial intelligence (GenAI) is increasingly used in mental health care, from client-facing chatbots to clinician-facing documentation aids. Psychotherapists’ willingness to rely on—or withhold reliance from—these tools has significant implications for care quality, yet little is known about how practicing clinicians calibrate trust and distrust in GenAI across tasks and contexts. Given that the […]
Accuracy of Radiomics-Based Machine Learning for Predicting Risk of Recurrence in Non–Small Cell Lung Cancer: Systematic Review and Meta-Analysis
Background: During the diagnosis and treatment of non–small cell lung cancer (NSCLC), detecting the risk of its recurrence in an early phase is still challenging. Recent studies have investigated the radiomics-based machine learning (ML) models for detecting the risk of recurrence in NSCLC. However, there is still insufficient systematic evidence to prove its efficiency. Objective: […]
Strategy for Hepatitis B and C Virus Testing Campaigns Through Web Services and Digital Advertising in Japan: Nationwide Cross-Sectional Study With Correspondence Analysis
Background: Public awareness campaigns and testing promotion must be strengthened to eliminate infections with hepatitis B and C viruses (HBV and HCV, respectively) by 2030. Although public health campaigns using various forms of advertising are widely implemented, the most appropriate channels for viral hepatitis testing remain unclear. Objective: This study aims to identify web services […]
The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis
Background: Digital biomarkers are increasingly being used to support depression assessment by providing objective, continuous, and real-time physiological and behavioral data. However, most existing studies have focused on individual biomarkers, such as sleep or cardiac parameters, while integrative evaluations that capture the multidimensional nature of depression remain limited. Objective: This systematic review evaluated digital biomarkers […]
Effectiveness of the Components of a Digital Multiple Health Behavior Change Intervention Among Individuals Seeking Help Online (Coach): Factorial Randomized Trial
Background: Extant digital multiple health behavior change interventions have shown promise in various populations; however, evidence for a broader approach among the general population is lacking. Moreover, existing interventions often contain several components but are typically assessed as a whole, meaning it remains unclear to what extent individual components contribute to intervention effects and how […]
Artificial Intelligence in Health Professions Education: Qualitative Study of Student Experiences
Background: Artificial intelligence (AI) is increasingly integrated into education and health care, raising questions about how students use these technologies and how AI influences their learning. In health education, understanding these trends is particularly important because student learning directly impacts future clinical skills. Objective: This study aimed to explore the use of AI tools by […]
Well-Being and Cognitive Factors Influencing Health Care Workers’ Adherence to Internet-Based Stress Management: Mixed Methods Analysis of a Nonrandomized Controlled Study
Background: High stress levels are common among health care workers (HCWs), threatening their health and workforce stability. Internet-based mobile stress management (MSM) is a promising intervention for reducing work-related stress; however, poor adherence limits effectiveness. Exploring factors influencing HCWs’ adherence may thus aid in developing optimal interventions. Objective: The research aimed to investigate (1) how […]
Ethical Handling of Occupational Health and Safety Data in the Fire Service: Empirical Interview and Focus Group Study of Firefighter and Fire Service Leadership Privacy Preferences
Background: There are ongoing efforts to collect larger and higher-quality amounts of occupational health and safety data to better understand and prevent injuries and fatalities among high-risk workers, such as firefighters. Digital health systems including wearable technologies, mobile apps, or internet-based data collection platforms could collect large amounts of sensitive data, but there is little […]
Semaglutide offered on nhs to cut heart attack and stroke risk
NHS England intends to provide the weight-loss drug semaglutide to over 1.2 million people with cardiovascular disease who are overweight, following guidance that demonstrates its effectiveness in reducing the risk of heart attacks and strokes People with heart and circulatory disease who are overweight will be able to receive Semaglutide from summer 2026, following approval […]
Emotion Entanglement and Bayesian Inference for Multi-Dimensional Emotion Understanding
arXiv:2604.00819v1 Announce Type: cross Abstract: Understanding emotions in natural language is inherently a multi-dimensional reasoning problem, where multiple affective signals interact through context, interpersonal relations, and situational cues. However, most existing emotion understanding benchmarks rely on short texts and predefined emotion labels, reducing this process to independent label prediction and ignoring the structured dependencies among […]