Insights from the JAMA Summit on AI in Health Care
AI in health care presents both opportunities and challenges. The JAMA Summit Report highlights expert insights on its future. 🤖💉
Orthopedic perioperative nursing under navigation nurse management: Machine learning-based risk prediction models for postoperative recovery quality and explainable artificial intelligence analysis.
"Machine Learning in Orthopedic Nursing: NNM Boosts Recovery Quality 📈🤖"
Artificial Intelligence in Inflammatory Bowel Disease Management.
Exploring AI's role in managing Inflammatory Bowel Disease: insights from Shang et al.'s 2025 study. 🤖📊
Culturally Tailored Tele-Mental Health Care Linkage for Indigenous Populations: Protocol for a Mixed Methods Pilot Study.
Culturally tailored tele-mental health care for Indigenous populations shows promise in addressing mental health disparities. 📱🌍
Secure and Intelligent Low-Altitude Infrastructures: Synergistic Integration of IoT Networks, AI Decision-Making and Blockchain Trust Mechanisms.
Exploring IoT, AI, and Blockchain in Low-Altitude Economies: A Comprehensive Review of Synergistic Technologies 🚁📊
New AI Tool Enhances Medical Imaging Efficiency by 90%
New AI tool, MetaSeg, improves medical imaging efficiency by 90%. It simplifies image segmentation for better diagnosis. 🧠💻
Current insights on predicting vestibular diseases using machine learning.
Exploring machine learning's role in predicting vestibular diseases: a transformative approach for early diagnosis and personalized care. 🤖🩺
AI Enhances Diagnostic Accuracy Using Electronic Health Records
AI improves diagnostic accuracy by analyzing electronic health records, revealing hidden patterns for better patient care. 🩺📊
Integrative multi-omic profiling in blood reveals distinct immune and metabolic signatures between ACPA-negative and ACPA-positive rheumatoid arthritis.
Distinct immune and metabolic profiles in ACPA-negative vs. ACPA-positive rheumatoid arthritis revealed through multi-omic blood analysis. 🩸🔬
Label-free histological identification of intraductal carcinoma of the prostate using texture analysis-based multimodal stimulated Raman scattering microscopy.
Innovative imaging techniques boost IDC-P detection accuracy to 98% using machine learning and texture analysis. 📊🔬