![[The analysis of invention patents in the field of artificial intelligent medical devices].](https://yesilscience.com/wp-content/uploads/2025/06/The-analysis-of-invention-patents-in-the-field-of-artificial-intelligent-medical-devices.jpg)
[The analysis of invention patents in the field of artificial intelligent medical devices].
Exploring AI Medical Devices: Patent Trends, Key Players, and Innovations 📈🤖
New AI Tool on NHS App Aims to Reduce Health Inequalities
New AI tool on NHS App aims to improve access to health information and reduce inequalities. 📱🤖
Forecasting Achievement of Inactive Disease in Juvenile Idiopathic Arthritis with Artificial Intelligence.
AI predicts inactive disease in juvenile idiopathic arthritis: 68 predictors, 414 patients, 0.68 MCC! 🤖📊
Patient Death Associated with Cyber Attack on NHS Pathology Provider
Patient death linked to NHS cyber attack raises concerns about healthcare cybersecurity. ⚠️ Urgent reviews and improvements needed. 🏥
Human-Centered Design and Development in Digital Health: Approaches, Challenges, and Emerging Trends.
Human-Centered Design in Digital Health: Key Trends & Challenges 🧠💻
NHS Wales Introduces New Diabetes Management Apps
NHS Wales launches diabetes management apps to support Type 2 diabetes prevention and self-management. Accessible tools aim to enhance patient care. 📱💙
Immunomodulatory Therapy in Non-Infectious Uveitis: Current Landscape, Gaps, and Future Directions.
Immunomodulatory therapy for non-infectious uveitis: addressing gaps, optimizing strategies, and future AI advancements. 🧠👁️
Need for Unified Patient Records in the NHS
Unified Patient Records in the NHS: A pressing need for better care and efficiency. 🏥📊
Leveling Up: Harnessing Cutting-Edge Technology to Enhance Oncology Education and Learning.
Revolutionizing oncology education with AI, gamification, and digital media enhances learning experiences and professional growth. 📚💻
![[A deep learning method for differentiating nasopharyngeal carcinoma and lymphoma based on MRI].](https://yesilscience.com/wp-content/uploads/2025/06/A-deep-learning-method-for-differentiating-nasopharyngeal-carcinoma-and-lymphoma-based-on-MRI.jpg)
[A deep learning method for differentiating nasopharyngeal carcinoma and lymphoma based on MRI].
Deep learning model differentiates nasopharyngeal carcinoma from lymphoma using MRI: AUC 0.93 for T1WI+T2WI. 📊🧠