Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, 3300 Doc J. Thurston Bldg, Campus Box #7280, Chapel Hill, NC, 27599-7280, USA.
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Curr Rheumatol Rep. 2023 Nov;25(11):213-225. doi: 10.1007/s11926-023-01114-9. Epub 2023 Aug 10.
Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and patients in decision making, based on machine-learned evidence, and (2) improve our understanding of pathophysiology and mechanisms underlying OA, providing new insights into disease management and prevention. The purpose of this review is to improve the ability of clinicians and OA researchers to understand the strengths and limitations of AI/ML methods in applications to OA research.
AI/ML can assist clinicians by prediction of OA incidence and progression and by providing tailored personalized treatment. These methods allow using multidimensional multi-source data to understand the nature of OA, to identify different OA phenotypes, and for biomarker discovery. We described the recent implementations of AI/ML in OA research and highlighted potential future directions and associated challenges.
骨关节炎（OA）是一种复杂的异质性疾病，目前尚无有效的治疗方法。人工智能（AI）及其子领域机器学习（ML）可应用于来自不同来源的数据，以（1）根据机器学习证据帮助临床医生和患者做出决策，以及（2）增进我们对 OA 病理生理学和发病机制的理解，为疾病管理和预防提供新的见解。本综述旨在提高临床医生和 OA 研究人员理解 AI/ML 方法在 OA 研究应用中的优势和局限性的能力。
AI/ML 可通过预测 OA 的发病和进展，以及提供量身定制的个性化治疗，来帮助临床医生。这些方法允许使用多维多源数据来了解 OA 的本质，识别不同的 OA 表型，并发现生物标志物。我们描述了 AI/ML 在 OA 研究中的最新应用，并强调了潜在的未来方向和相关挑战。
