DHSC and NHSE Commit to £7.4 Billion Digital Investment by 2030
DHSC and NHSE plan a £7.4 billion investment in digital health by 2030. This aims to enhance technology and data use. 💻📈
Investigating Placebos and Controls Used in Large Language Model-Based Chatbot Intervention Trials: Protocol for a Methodological Review.
Exploring LLM Chatbot Trials: Control Conditions Matter! 🤖📊
Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification.
AI in ECG: Revolutionizing heart failure screening & risk stratification. Key findings from Dhingra et al. 📈❤️
Cyber Attack Disrupts NHS Medical Equipment Supply
Cyber attack impacts NHS medical equipment supply, affecting defibrillators and more. ⚠️🩺 Stay informed on healthcare disruptions.
Artificial intelligence-based volumetric measurements for longitudinal clinical assessment of treatment response in high-grade gliomas: Validation across institutional and public datasets.
AI in High-Grade Glioma Treatment: 1648 MRI Sequences Analyzed, 15s Segmentation Time, Moderate Agreement with MDTB Diagnoses 📊🧠
Non-Contact Analysis of Cardiopulmonary Coupling via Video Technology
Non-contact video technology analyzes cardiopulmonary coupling, linking heartbeats and breathing for insights on health and sleep quality. 🫀💤
Addressing Vulnerabilities in Ambient Voice Technology
Ambient voice technology in healthcare has vulnerabilities. Yvette Khozam emphasizes the need for secure clinical notes. 🔒🩺
An Integrated Risk Prediction Model for Gout Using Clinical Data, Ultrasound Features, and Deep Learning: A Retrospective Multicenter Study.
Innovative gout risk model combines clinical data, ultrasound, and deep learning, achieving AUC of 0.904! 📊🦵
New LLM Accurately Detects Depression in Women Through WhatsApp Voice Messages
New LLM detects depression in women via WhatsApp voice messages with over 91% accuracy. 📱🧠
Personalizing fall fear prevention in knee osteoarthritis: an interpretable prediction framework via IGKSO synchronous optimization and SHAP-based risk stratification.
Personalized fall fear prediction in knee osteoarthritis: 541 patients, 0.8842 F1 score, 0.9451 AUC. Key risk factors identified! 📊👩⚕️