FDP Benefits Limited by Central Clarity Issues
FDP benefits are hindered by unclear guidance from central authorities, impacting digital health advancements. 🏥🔍
Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.
Radiomics & AI enhance tumor response prediction in GI neoplasms. Key findings: ORs range from 8.12 to 11.62. 📊🔍
Comment on Topaloglu et al. Machine Learning-Driven Lung Sound Analysis: Novel Methodology for Asthma Diagnosis. Adv. Respir. Med. 2025, 93, 32.
Exploring machine learning in asthma diagnosis: Topaloglu et al.'s innovative lung sound analysis method. 📊🫁
Communication, Compassion, and Shared Decision-Making in Allergy.
Exploring SDM and compassion in allergy care enhances patient outcomes and trust. Key for effective treatment! 🌟🤝
Shifting Focus: The Rise of WellTech in Health Innovation
WellTech emphasizes patient involvement in healthcare, promoting collaboration between individuals and providers for better health outcomes. 🏥🤝
Brainstorming Entrepreneurship in Brain Health.
Exploring brain health innovation: insights from the Brainstorme! forum on diagnostics, digital solutions, and entrepreneurial strategies. 🧠💡
Validity and Reliability of an AI-Based Jump Height App vs. Infrared Contact Mat: Minimal Influence of Skin Pigmentation Under Standardized Lighting.
AI Jump Height App vs. Infrared Mat: Validity (R²=0.94) & Reliability (ICC=0.97) Confirmed! 📊🤖
Machine learning approached a 14-item shortened version of the Positive And Negative Sleep Appraisal Measure (PANSAM-14).
Machine learning refines sleep appraisal: PANSAM-14 shows R² scores up to 0.95 for predictive accuracy. 💤📊
NHS Funding Prioritizes Non-Acute Care Settings
NHS funding will increasingly support non-acute care settings, as confirmed by Alec Price-Forbes at Rewired 2026. 🏥💰
Extubation Decision Support in Critical Care: A Multimodal Machine Learning Framework Integrating Segmented Radiographs and Routine Clinical Data.
Multimodal ML enhances extubation decisions in critical care, achieving 79.46% accuracy. Key metrics: respiratory rates, tidal volumes, demographics. 📊🩺