yesilscience - Yesil Science

Factors associated with the experience of AI tools for creating health education materials: cross-sectional study using an extended UTAUT model.

Factors associated with the experience of AI tools for creating health education materials: cross-sectional study using an extended UTAUT model.

AI tools in health education: 45.4% of medical students use them. Key factors: social influence, facilitating conditions, and training. 📚🤖

January 5, 2026

Weekly Health & AI Digest – January 05, 2026

📰 News

Weekly Health & AI Digest – January 05, 2026

Here are the latest breakthroughs in health & AI (January 05, 2026 week) 🔹 Characterizing immune cell profiles of patients with rheumatoid arthritis in Taiwan using artificial intelligence-based cytometric approaches. AI identifies 16 immune cell subsets in rheumatoid arthritis with...

January 5, 2026

Characterizing immune cell profiles of patients with rheumatoid arthritis in Taiwan using artificial intelligence-based cytometric approaches.

⚖️ AI Ethics 💉 Allergy & Immunology 🧠 Digital Therapeutics

Characterizing immune cell profiles of patients with rheumatoid arthritis in Taiwan using artificial intelligence-based cytometric approaches.

AI identifies 16 immune cell subsets in rheumatoid arthritis with 100% sensitivity and specificity. Key findings on immune responses. 🧬🔬

January 4, 2026

Large Language Model-Based Virtual Patient Systems for History-Taking in Medical Education: Comprehensive Systematic Review.

🧠 LLM's 🤖 Machine Learning 🧠 Mental Health

Large Language Model-Based Virtual Patient Systems for History-Taking in Medical Education: Comprehensive Systematic Review.

Exploring LLMs in Medical Education: 39 studies reveal strong performance but limited multimorbidity representation. 📊🤖

January 4, 2026

Developing ultrasound-based machine learning models for accurate differentiation between sclerosing adenosis and invasive ductal carcinoma.

💼 Digital Health Entrepreneurship 🤖 Machine Learning

Developing ultrasound-based machine learning models for accurate differentiation between sclerosing adenosis and invasive ductal carcinoma.

Machine learning enhances ultrasound accuracy in distinguishing sclerosing adenosis from invasive ductal carcinoma. 📊🔍

January 3, 2026

The Application of Mobile Health in Self-Management Among Patients Undergoing Dialysis: Scoping Review.

🤖 Internet of Things (IoT)📱 Mobile Health 🩺 Preventive Medicine 🌐 Telehealth

The Application of Mobile Health in Self-Management Among Patients Undergoing Dialysis: Scoping Review.

Mobile health enhances dialysis self-management, focusing on diet, medication, and psychological support. 📱💉 Key findings from 34 studies.

January 3, 2026

Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.

💼 Health Entrepreneurship 🤖 Machine Learning 🧠 Mental Health

Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.

AI in ADHD Care: Study shows efficiency gains but potential quality trade-offs in therapy delivery. 🤖📉

January 3, 2026

Health Tech Suppliers Share Insights for 2026

⚖️ AI Ethics 🧠 Brain-Computer Interfaces 🖼️ Computer Vision 🦷 Dentistry 💼 Digital Health Entrepreneurship 🧠 Digital Therapeutics 👵 Elderly Care 🩹 Emergency 🩺 Endocrinology 🦠 Epidemiology 🏋️ Fitness & Sports 🏥 General Practice 🏥 General Surgery 🧬 Genetics 💵 Health Economics 💼 Health Entrepreneurship 🩸 Hematology 🏥 Hospital Management 🤖 Internet of Things (IoT)🧪 Lab Medicine 🤖 Machine Learning 🧠 Mental Health 🦠 Microbiology 📱 Mobile Health 🧠 Neuroscience 📰 News 🥗 Nutrition 🦴 Orthopedics 🛌 Palliative Care 👶 Pediatrics 💊 Pharmacy 🩺 Preventive Medicine 🧠 Psychiatry 🌍 Public Health 🧬 Rare Diseases 🗣️ Speech Recognition 🗣️ Speech-Language 🌐 Telehealth 💉 Vaccination 🏢 Work Health 🧑‍🏫 Health Education 🧑‍🦽 Rehabilitation

Health Tech Suppliers Share Insights for 2026

Health tech suppliers predict significant advancements in NHS technology by 2026, focusing on AI integration, patient-centered care, and improved data management. 🏥💻

January 2, 2026

Bibliometric analysis of trends, innovations, and the future of CBT-based mobile interventions for depression.

⌚ Wearables 🧠 Digital Therapeutics 🤖 Machine Learning 📱 Mobile Health

Bibliometric analysis of trends, innovations, and the future of CBT-based mobile interventions for depression.

📈 CBT Mobile Interventions: 350 studies, 72 trials, AI integration, and global trends in depression treatment. 📱

January 2, 2026

Predictions for Digital Health Innovations in 2026

⚖️ AI Ethics 💼 Health Entrepreneurship 🤖 Machine Learning 📰 News 🩺 Preventive Medicine 🌐 Telehealth

Predictions for Digital Health Innovations in 2026

Digital health innovations are expected to advance significantly by 2026, focusing on AI, data integration, and patient care improvements. 📈💻

January 2, 2026

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Author: yesilscience

Factors associated with the experience of AI tools for creating health education materials: cross-sectional study using an extended UTAUT model.

Weekly Health & AI Digest – January 05, 2026

Characterizing immune cell profiles of patients with rheumatoid arthritis in Taiwan using artificial intelligence-based cytometric approaches.

Large Language Model-Based Virtual Patient Systems for History-Taking in Medical Education: Comprehensive Systematic Review.

Developing ultrasound-based machine learning models for accurate differentiation between sclerosing adenosis and invasive ductal carcinoma.

The Application of Mobile Health in Self-Management Among Patients Undergoing Dialysis: Scoping Review.

Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.

Health Tech Suppliers Share Insights for 2026

Bibliometric analysis of trends, innovations, and the future of CBT-based mobile interventions for depression.

Predictions for Digital Health Innovations in 2026

Compact large language models for title and abstract screening in systematic reviews: An assessment of feasibility, accuracy, and workload reduction.

Patients Can Enroll in Cancer Trials Through NHS App

Scottish GPs Highlight Need for Improved IT Systems

Diagnostic performance of convolutional neural network-based AI in detecting oral squamous cell carcinoma: a meta-analysis.

Use of Automation Technologies and Data Mining in Speech Recognition for Autism.

Bridging machine learning and clinical practice: a multicentre nomogram for 90-day graft failure risk stratification in heart transplantation.

Future trends and challenges in occupational therapy: Insights from a Delphi study.

Classification of angioedema types using decision tree modeling.

Northamptonshire’s Shared Care Record Project Expected to Save Over £1 Million

[Reconstruction of superficial organs: a leap from structural restoration to functional rehabilitation].