Shift Towards Digital and Community Care in NHS, According to Wes Streeting
NHS plans to shift care from hospitals to digital and community settings, emphasizing technology and AI integration. 📱🏥
Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis.
Innovative AI fusion boosts gastric cancer detection accuracy to 97.8% 📈, enhancing clinical trust with explainable insights! 🩺
Alzheimer’s Society Accelerator Chooses Two Innovations for Dementia Care
Alzheimer’s Society Accelerator selects Neu Health and Hydro Care for dementia care innovations. 📱💧 Both aim to improve patient monitoring and hydration.
Impact of a deep-learning image reconstruction algorithm on image quality and detection of solid lung lesions.
Deep-learning algorithms enhance lung lesion detection in CT scans, improving image quality significantly. 📈🩻
Enhanced Error Detection in Radiology Reports Using Fine-Tuned LLMs
Fine-tuned LLMs improve error detection in radiology reports, enhancing patient care and reducing cognitive load for radiologists. 🩻📊
Knowledge Graph-Enhanced Deep Learning Model (H-SYSTEM) for Hypertensive Intracerebral Hemorrhage: Model Development and Validation.
AI-Driven H-SYSTEM Achieves 94.87% Accuracy in Hypertensive Intracerebral Hemorrhage Diagnosis 🧠📊
Joint Commission and CHAI Collaborate on AI Best Practices for Healthcare
Joint Commission collaborates with CHAI to establish AI best practices in healthcare. Guidance expected by fall 2025. 🤖🏥
Recommendations for Successful Development and Implementation of Digital Health Technology Tools.
Digital health tools can transform care delivery. Key recommendations include interdisciplinary teams, patient feedback, and ethical considerations. 📊💡
Machine Learning Tool Developed for Colorectal Cancer Diagnosis and Monitoring
New machine learning tool aids in colorectal cancer diagnosis and monitoring by analyzing metabolic profiles. 🧬🔍
Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study.
Machine learning predicts occupational noise-induced hearing loss using blood indicators. AUC 0.942, sensitivity 0.875, specificity 0.936. 📊🔍