Gilvaz Vinit J, Sudheer Aishwarya, Reginato Anthony M
Division of Rheumatology, Department of Internal Medicine, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA.
Department of Internal Medicine, Saint Vincent Hospital, Worcester, MA, USA.
Curr Rheumatol Rep. 2025 Jun 28;27(1):28. doi: 10.1007/s11926-025-01193-w.
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.
撰写本综述是为了用通俗易懂且实用的语言，向临床风湿病医生介绍基于人工智能（AI）的类风湿关节炎（RA）研究的最新进展。我们重点介绍了2023年至2025年初在诊断成像、治疗预测、药物研发以及面向患者的工具等方面的进展。鉴于临床对AI的兴趣日益浓厚及其在改善医疗服务方面的潜力，本文旨在弥合技术创新与实际风湿病学实践之间的差距。
几种AI模型在使用热成像和核扫描等成像方式检测早期RA方面已显示出高准确性。治疗反应预测模型利用了常规收集的电子健康记录（EHR）数据，正朝着在临床工作流程中的实际应用迈进。诸如移动症状检查器之类的面向患者的工具以及像ChatGPT这样的大语言模型（LLM）在加强教育和参与方面显示出前景，尽管准确性和安全性仍存在差异。AI在识别新型生物标志物和加速药物研发方面也已显示出效用。尽管有这些进展，但截至2025年初，与其他专科不同，尚无基于AI的工具获得FDA批准用于风湿病学。人工智能在改善RA的临床护理方面——从早期诊断到个性化治疗——具有巨大潜力。然而，由于监管、技术和实施方面的挑战，临床应用仍然有限。迫切需要一个简化的监管框架以及临床医生、研究人员和行业合作伙伴之间更紧密的合作。通过深思熟虑的整合，AI可以成为应对风湿病学临床复杂性和劳动力短缺问题的有价值辅助手段。
