International Journal of Clinical Drug Practice & Pharmacotherapy

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by pain, joint destruction, and impaired function, for which pharmacologic therapy is foundational but often insufficient to address long term disability and quality of life concerns. Traditional Chinese mind-body practices such as Tai Chi and Qigong have been proposed as low impact complementary therapies, yet the evidence specific to RA remains limited and scattered across heterogeneous studies. This paper uses an artificial intelligence-assisted approach to identify and summarize PubMed indexed studies of Tai Chi and related mind-body exercise in RA, including systematic reviews, Cochrane analyses, observational cohorts, mixed methods studies, and narrative reviews. The AI generated summaries highlight modest improvements in pain, physical function, mood, and vascular parameters in some trials, alongside consistent signals of safety but overall low or very low certainty of evidence because of small sample sizes, risk of bias, and methodological limitations. Taken together, the findings suggest that Tai Chi is a feasible and generally safe adjunct to standard RA care, with potential benefits for selected patient reported and physiological outcomes, but they also underscore the need for larger, rigorously designed randomized controlled trials and more standardized intervention protocols. The study further illustrates how artificial intelligence can efficiently synthesize emerging literature on Traditional Chinese Medicine modalities and support clinicians and researchers in appraising complementary therapies for chronic rheumatologic disease.