The following is a summary of “Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions” published in the November 2022 issue of Rheumatology by Nelson, et al. 

In recent years, there has been a significant increase in the application of artificial intelligence (AI) analytics in the field of medicine, specifically in the diagnosis and treatment of rheumatic and musculoskeletal illnesses (RMDs). One area in particular that has benefited greatly from this trend is the field of rheumatology. 

These types of methodologies present a problem for medical professionals, patients, and researchers because most algorithms have a “black box” quality, the terminology themselves are foreign, and there is a lack of awareness of the potential risks associated with these studies. As a consequence of this, the objective of this review is to present an introduction to this subject in a manner that is suitable for both they and medical professionals in terms of its applicability and significance. 

Investigators 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. In addition, they hope to provide some recent examples of such analyses. In addition, one of their goals is to present some current case studies of analysis of this kind.