Signals shaping
health AI.
A focused stream of research, industry developments and ideas selected for people building the future of health.
Research and developments, with a clear point of view.
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...
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...
Health Tech Suppliers Share Insights for 2026
Health tech suppliers predict significant advancements in NHS technology by 2026, focusing on AI...
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...
Predictions for Digital Health Innovations in 2026
Digital health innovations are expected to advance significantly by 2026, focusing on AI, data...
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...
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...
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...
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...