Analysis and Mapping of Machine Learning in the Context of Diabetes.
Machine learning in diabetes research: 5,222 studies analyzed, USA leads in publications. 📈🤖
Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs.
AI boosts pelvic X-ray accuracy by 8% and cuts reading time by 57% in trauma assessments. 📈🩺
Economic Evaluations and Equity in the Use of Artificial Intelligence in Imaging Examinations for Medical Diagnosis in People With Dermatological, Neurological, and Pulmonary Diseases: Systematic Review.
AI in Medical Imaging: Economic Benefits & Equity Challenges 📊🤖
Automatic segmentation of clear cell renal cell carcinoma based on deep learning and a preliminary exploration of the tumor microenvironment.
Deep learning enhances ccRCC diagnosis with 96.67% accuracy, revealing TME's role in immunotherapy response. 📊🧬
An adjuvant database for preclinical evaluation of vaccines and immunotherapeutics.
New adjuvant database enhances vaccine evaluation with transcriptomic data, predicting efficacy and toxicity across 25 adjuvants. 📊💉
Elucidating Celecoxib’s Preventive Effect in Capecitabine-Induced Hand-Foot Syndrome Using Medical Natural Language Processing.
Celecoxib may reduce capecitabine-induced hand-foot syndrome risk by 49%. NLP effectively identifies HFS in clinical records. 📊💊
Online Self-Referral for Midwife Appointments Now Available for Pregnant Women in England
Online self-referral for midwife appointments is now available for pregnant women in England. 🌼 This simplifies access to maternity care. 🏥
Exploring fecal microbiota signatures associated with immune response and antibiotic impact in NSCLC: insights from metagenomic and machine learning approaches.
Fecal microbiota impacts lung cancer immunotherapy response. Key taxa identified: Bacteroides caccae, Prevotella copri. 📊🦠
Integrating mHealth Innovations into Decentralized Oncology Trials.
Integrating mHealth in oncology trials boosts patient engagement and data collection. 📱💉 Key for equitable access!
Asthma Detection Research Based on Voice Signal Processing and Machine Learning.
Voice analysis for asthma detection: 87% accuracy using machine learning! 📊🔍 400+ voice features analyzed. Significant findings! 🗣️💡