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

Abstract

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.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

“…Another review, published in the middle of the year, discussed recent technologies and innovations that are expected to benefit clinical practice in the early 2030s, with regard to big data [42]. A narrative review of ML in RMDs for clinicians and researchers was published by Nelson AE et al [43]. Furthermore, another narrative review [44] covered the applications of ML in systemic sclerosis (SS).…”