Discover the newest research about AI innovations in π Rheumatology.
Distinct immune and metabolic profiles in ACPA-negative vs. ACPA-positive rheumatoid arthritis revealed through multi-omic blood analysis. π©Έπ¬
Machine learning predicts difficult-to-treat rheumatoid arthritis with 0.606-0.747 accuracy. Key features include DAS28-ESR and HAQ. ππ€
Exploring childhood vasculitis: recent advances in diagnosis, treatment, and future directions. ππ©Ί
Integrating AI in medical training enhances skills for future doctors. Key insights from Tan et al. (2025) reveal transformative potential. π€π
Exploring biomarkers for rheumatoid arthritis treatment response: 10-15% of patients may benefit from early cellular therapies. π¬π
Neural networks predict knee osteoarthritis progression with 0.913 AUC accuracy using MRI radiomics and biomarkers. ππ¦΅
AI-based cartilage analysis shows FLASH MRI sensitivity at -0.81, slightly below manual methods. Key findings from PubMed review. ππ¦΄
Global Psoriatic Arthritis Guidelines: Key Insights from ACR, EULAR, and GRAPPA π©Ίπ
Philips Foundation’s 2024 report shows healthcare access for 46.5 million people, up from 28 million. ππ
Machine learning predicts juvenile idiopathic arthritis in children: 223,195 analyzed, AUC up to 0.9130! ππ€
