The Application of Mobile Health in Self-Management Among Patients Undergoing Dialysis: Scoping Review.
Mobile health enhances dialysis self-management, focusing on diet, medication, and psychological support. 📱💉 Key findings from 34 studies.
Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.
AI in ADHD Care: Study shows efficiency gains but potential quality trade-offs in therapy delivery. 🤖📉
Health Tech Suppliers Share Insights for 2026
Health tech suppliers predict significant advancements in NHS technology by 2026, focusing on AI integration, patient-centered care, and improved data management. 🏥💻
Bibliometric analysis of trends, innovations, and the future of CBT-based mobile interventions for depression.
📈 CBT Mobile Interventions: 350 studies, 72 trials, AI integration, and global trends in depression treatment. 📱
Predictions for Digital Health Innovations in 2026
Digital health innovations are expected to advance significantly by 2026, focusing on AI, data integration, and patient care improvements. 📈💻
A full-automated tumor budding annotation approach in hematoxylin and eosin-stained whole slide images of colorectal cancer.
Automated tumor budding annotation in colorectal cancer shows AUCs up to 0.988 and average grading time of 21 seconds. 📊🩺
Bridging the gap in digital health: A framework for leveraging digital health technologies in cardiovascular diseases, hypertension, and diabetes-a narrative review.
Digital health technologies transform chronic disease management. Key pillars include AI, interoperability, and privacy solutions. 📊💡
Artificial Intelligence-Integrated Biosensors for Antimicrobial Resistance Detection and Surveillance: A Review and Future Perspectives for Global Biosecurity.
AI biosensors revolutionize AMR detection, enhancing global health surveillance with real-time insights and predictive analytics. 📊🔬
Development of machine learning models to predict risk of hospitalisation and 90-day readmission among patients with cardiovascular risk factors using community health survey data.
Machine learning predicts hospitalizations and readmissions in cardiovascular patients with 93% accuracy. Key factors include heart disease and medication burden. 📊❤️
A Multimodal Drug-Target Affinity Prediction Framework with Pretrained Models and Hierarchical Graph Transformer.
Revolutionary DTA Prediction: PMHGT-DTA Framework Enhances Drug Discovery with 3D Graphs and Cross-Attention! 💊🔬