Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis.
COVID-19 vaccine hesitancy in China: key factors include age, social norms, and vaccine knowledge. 📊💉
Collaboration Between UHL, UHN, and Microsoft to Enhance AI in Healthcare
UHL, UHN, and Microsoft partner to advance AI in healthcare, aiming to improve patient care and streamline services. 🤝💻
Empowering tomorrow’s public health researchers and clinicians to develop digital health interventions using chatbots, virtual reality, and other AI technologies.
Exploring AI in Public Health Education: Chatbots & VR Integration 📚🤖
Introducing Personalized Digital Twins for Health Management
Personalized digital twins may enhance health management by predicting disease risks and optimizing treatments. 🤖💊
Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models.
Hybrid deep learning models boost BCI accuracy to 96.06% in EEG classification, outperforming traditional methods. 🧠📊
Diagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning model.
Deep learning enhances UVFP diagnosis: 99% accuracy with video analysis vs. 98% with images. Significant clinical implications! 📈🎤
AI Identifies New Antibiotic Candidates from Venom
AI identifies potential antibiotics from snake and spider venom, offering hope against antibiotic resistance. 🐍🕷️💊
Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins.
Exploring AI's role in predicting biological pathways through intrinsically disordered proteins. 🤖🧬 Key insights from recent research!
AI Effectively Classifies Pancreatic Cysts Using Imaging Data
AI accurately classifies pancreatic cysts using MRI and CT scans, achieving up to 99% accuracy. 🩻🤖
The Artificial Intelligence-Assisted Diagnosis of Skeletal Dysplasias in Pediatric Patients: A Comparative Benchmark Study of Large Language Models and a Clinical Expert Group.
AI models show 62.2% accuracy in diagnosing skeletal dysplasias, compared to 82.2% by clinical experts. 🤖📊