Addressing the Challenges of AI in Healthcare
AI in healthcare faces challenges like data integration and scalability. Future agentic AIs must prioritize transparency for patient safety. 🏥🤖
Sphenoid sinus hyperpneumatization: anatomical variants, molecular blueprints, and AI-augmented roadmaps for skull base surgery.
Sphenoid sinus hyperpneumatization impacts surgical safety; AI and advanced imaging enhance precision in skull base surgery. 🧠🔍
AI Tool Accurately Differentiates Glioblastoma from Similar Brain Tumors During Surgery
AI tool PICTURE accurately differentiates glioblastoma from similar brain tumors during surgery, aiding timely treatment decisions. 🧠🔍
Foreign body removal.
Airway foreign body aspiration poses risks, especially in children and seniors. Recent studies show 98-99% diagnostic accuracy with advanced imaging. 🩺🔍
Non-invasive assessment techniques for renal fibrosis: advances and perspectives.
Non-invasive renal fibrosis assessment: AI, biomarkers, and imaging techniques show promise for CKD diagnosis. 📊🩺
Emerging concepts in the diagnosis and management of metabolically associated steatotic liver disease.
Emerging strategies for diagnosing and managing MASLD: noninvasive methods, novel therapies, and personalized approaches. 🩺📊
Predicting Negative Self-Rated Oral Health in Adults Using Machine Learning: A Longitudinal Study in Southern Brazil.
Machine learning predicts negative self-rated oral health in adults with 16.6% prevalence. Key predictors include socioeconomic status and anxiety. 📊🦷
Anston attentional network for structured data based stroke risk prediction in smart aging.
Anston model achieves 95% accuracy in predicting stroke risks for elderly care using structured data. 📊👵
AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment.
AI Enhances Cardiovascular Risk Assessment via Visceral Fat Imaging 📊🤖
Machine learning-based estimation of the mild cognitive impairment stage using multimodal physical and behavioral measures.
Machine learning predicts mild cognitive impairment stages with 94% accuracy using gait, body composition, and sleep data. 🧠📊