Emerging Artificial Intelligence Innovations in Rheumatoid Arthritis and Challenges to Clinical Adoption.
Authors
Affiliations (4)
Affiliations (4)
- Division of Rheumatology, Department of Internal Medicine, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA. [email protected].
- Department of Internal Medicine, Saint Vincent Hospital, Worcester, MA, USA.
- Division of Rheumatology, Department of Internal Medicine, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA.
- Department of Dermatology, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA.
Abstract
This review was written to inform practicing clinical rheumatologists about recent advances in artificial intelligence (AI) based research in rheumatoid arthritis (RA), using accessible and practical language. We highlight developments from 2023 to early 2025 across diagnostic imaging, treatment prediction, drug discovery, and patient-facing tools. Given the increasing clinical interest in AI and its potential to augment care delivery, this article aims to bridge the gap between technical innovation and real-world rheumatology practice. Several AI models have demonstrated high accuracy in early RA detection using imaging modalities such as thermal imaging and nuclear scans. Predictive models for treatment response have leveraged routinely collected electronic health record (EHR) data, moving closer to practical application in clinical workflows. Patient-facing tools like mobile symptom checkers and large language models (LLMs) such as ChatGPT show promise in enhancing education and engagement, although accuracy and safety remain variable. AI has also shown utility in identifying novel biomarkers and accelerating drug discovery. Despite these advances, as of early 2025, no AI-based tools have received FDA approval for use in rheumatology, in contrast to other specialties. Artificial intelligence holds tremendous promise to enhance clinical care in RA-from early diagnosis to personalized therapy. However, clinical adoption remains limited due to regulatory, technical, and implementation challenges. A streamlined regulatory framework and closer collaboration between clinicians, researchers, and industry partners are urgently needed. With thoughtful integration, AI can serve as a valuable adjunct in addressing clinical complexity and workforce shortages in rheumatology.