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

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.

Automated summary

There has been rapid growth in the use of artificial intelligence (AI) analytics in medicine, including in rheumatic and musculoskeletal diseases (RMDs). However, the black box nature of most algorithms and the unfamiliarity of the terms pose challenges for clinicians, patients, and researchers. This review aims to provide relevant insights into the strengths and limitations of AI analytics in RMDs, as well as recent examples and future directions in diagnosis, phenotyping, prognosis, and precision medicine.