Machine learning in rheumatology: The emerging cutting-edge strategy

Tamer A Gheita1*, Nevin Hammam2

Machine learning (ML) is a computerized analytical technique that is being increasingly used in biomedicine. ML often provides an advantage over ordinary programmed statisticsin the analysis of big and interrelated information. With the increasing availability of large rheumatology biomedical data, numerous studies have employed ML in rheumatology using electronic health records, imaging, or gene expression data. However, the use of ML has its current strengths and weakness in biomedicine. A better understanding of ML and the future application of advanced ML techniques alongside with the increasing availability of medical large data may facilitate the development of meaningful precision medicine for patients with rheumatic diseases (RDs). In this editorial, we describe the principles of ML, discuss examples of ML application in rheumatology, and illustrate the strengths and weakness of ML.