Can machine learning aid clinicians in diagnosing systemic lupus erythematosus? Adamichou et al. designed an algorithm that uses classical features of lupus to simulate medical reasoning and identify lupus early in the disease process. They were able to validate the algorithm, which demonstrated high sensitivity, specificity and accuracy.
As the capabilities of machine learning and artificial intelligence improve, rheumatologists have access to more data than ever, which may enable them to better predict which patients will respond to specific treatments, such as tumor necrosis factor inhibitors.
Machine learning is a tool that may help pediatric rheumatologists distinguish between different subtypes of juvenile idiopathic arthritis (JIA) and predict treatment response.
Machine learning, immunology, the metabolism—are just a few of the topics addressed during…
Discover the latest ILD management strategies for RA, SSc & myositis, plus expert guidance on pregnancy & defining progressive pulmonary fibrosis.
The gut microbiome may trigger RA & predict methotrexate response. ACR Convergence 2025 experts discuss pathogenesis & drug efficacy.
A rheumatologist with a background in biostatistics, data science & bioinformatics discusses the use of artificial intelligence in clinical research.
Incoming Arthritis & Rheumatology Editor-in-Chief Dr. S. Louis Bridges Jr., MD, PhD, discusses his path to rheumatology and outlines his vision for the journal.
I often think about medical literature as a sprawling metropolis. There are towering skyscrapers of randomized controlled trials, lofty schools of systematic reviews and meta-analyses, and verdant parks of qualitative studies. Much less assuming are the case reports, which are sort of like homesteads for the majority of people who publish and contribute to the…
Guest columnists Dr. Daniel Solomon & Dr. Andrew Concoff discuss the potential of technology, such as mobile health apps, to enhance remote care & improve access for underserved patients.