ACR Journals On Air

In this episode, we explore how artificial intelligence and machine learning are advancing the use of electronic health record data in rheumatology. Dr. Kat discusses the development and validation of a natural language processing–based model to infer rheumatoid arthritis disease activity from structured and unstructured clinical data, its application in studying cardiovascular outcomes, and the challenges of deploying predictive models across different health systems. The conversation also examines the future of real-world evidence generation, the practical limitations of AI in clinical research, and Dr. Kat's career journey, offering insights and advice for the next generation of physician-scientists.

In this episode, Michael J. Ombrello, MD, explores how our understanding of Still's disease has evolved, from recognizing interstitial lung disease as a key manifestation to uncovering the immunologic and genetic factors that may shape disease severity. He discusses insights from a National Institutes of Health natural history study, including interferon signatures, HLA associations, drug-associated immunologic reactions, and exome sequencing, and explains what these findings could mean for future diagnosis, risk stratification, and treatment. The conversation also turns to Dr. Ombrello's own career path, the personal experiences that inspire his research, and the mentors who helped shape his journey as a physician-scientist. 
Type I Interferon Signature is Associated With Lung Disease, Drug-Associated Immune Reactions, and Genetic Variation in Interferon-Linked Pathways in Still Disease

In this episode, Nicholas P. McCormic discusses new real-world research from the RISE registry examining the use of GLP-1 receptor agonists in patients with rheumatic and musculoskeletal diseases. He explains the motivation behind the study, its design, and key findings on medication use, weight loss, and differences between semaglutide and tirzepatide, while highlighting important considerations for clinicians and patients. Nick also shares insights into the strengths and limitations of using registry data before reflecting on his journey from psychology and sociology to earning a Ph.D. focused on chronic disease management and rheumatology, offering advice for aspiring researchers and a look at his future work.

In this episode, we talk with Dr. Ji Soo Kim about a new tool designed to help people with scleroderma better understand and manage their disease. Using information collected over many years from patients receiving routine care, Dr. Kim developed an interactive platform that turns complex medical data into easy-to-understand charts and predictions. We discuss how the tool can help patients and doctors track disease progression, compare experiences with similar patients, and make more informed treatment decisions together. Dr. Kim also shares insights into the challenges of working with real-world health data, the future of personalized medicine in rheumatology, and the career path that led to this innovative work. 
Development of a Personalized Visualization and Analysis Tool to Improve Clinical Care in Complex Multisystem Diseases With Application to Scleroderma

In this episode, we speak with Dr. Swati Mehta and Dr. Lee Schulman Shapiro about their study examining early- and late-onset scleroderma renal crisis using data from three patient cohorts. They discuss key clinical findings, including important differences related to sex, autoantibodies, presentation, dialysis outcomes, and long-term prognosis, as well as the implications for patient care. The conversation also explores the personal journeys of both guests—from Dr. Mehta's path from northern India to a career at the intersection of nephrology and rheumatology, to Dr. Shapiro's reflections on nearly five decades in clinical practice and life after retirement.