Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.

Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.
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DOI:
10.3899/jrheum.220326
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发表时间:
2022-11
期刊:
The Journal of rheumatology
影响因子:
--
通讯作者:
Arbeeva L
Arbeeva L
中科院分区:
其他
文献类型:
--
作者:
Nelson AE;Arbeeva L

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近年来,人工智能分析在医学中的应用迅速增长,包括风湿性和肌肉骨骼疾病(RMD)。考虑到大多数算法的“黑匣子”性质以及对术语的不熟悉和对这些分析的潜在问题缺乏认识,这些方法对临床医生、患者和研究人员来说是一个挑战。因此,本综述旨在以一种对临床医生和研究人员相关且有意义的方式介绍这一领域。我们希望提供一些关于相关优势和局限性、报告指南以及在关键领域进行此类分析的最新实例的见解,重点是在RMD的诊断、表型分析、预后和精准医学方面的经验教训和未来方向。
There has been rapid growth in the use of artificial intelligence 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 and the unfamiliarity of the terms and lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this 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.
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