A.E. Nelson, MD, MSCR, Department of Medicine, Division of Rheumatology, Allergy, and Immunology, University of North Carolina at Chapel Hill;
L. Arbeeva, MS, Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
J Rheumatol. 2022 Nov;49(11):1191-1200. doi: 10.3899/jrheum.220326. Epub 2022 Jul 15.
There has been rapid growth in the use of artificial intelligence (AI) analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers, given the "black box" nature of most algorithms, the unfamiliarity of the terms, and the lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this subject area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas, with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.
近年来，人工智能（AI）分析在医学中的应用迅速增长，包括在风湿和肌肉骨骼疾病（RMDs）中。鉴于大多数算法的“黑盒”性质、术语的不熟悉以及对这些分析潜在问题的认识不足，这些方法对临床医生、患者和研究人员构成了挑战。因此，本综述旨在以与临床医生和研究人员相关且有意义的方式介绍这一主题领域。我们希望提供一些关于相关优势和局限性、报告准则的见解，以及在关键领域中此类分析的最新示例，重点是在 RMD 中的诊断、表型、预后和精准医学方面的经验教训和未来方向。
