Applications and prospects of machine learning in the diagnosis and treatment of SLE

Abstract

Systemic lupus erythematosus (SLE) remains a major clinical challenge due to its broad phenotypic variability, unpredictable disease trajectory, and inconsistent therapeutic responses. The limitations of conventional diagnostic and prognostic tools underscore the need for more precise, data-driven approaches to support clinical decision-making. Artificial intelligence-based methods, including machine learning (ML) have emerged as powerful technologies capable of analyzing complex, high-dimensional data to reveal hidden patterns and enhance disease understanding. These methods are particularly well-suited for tackling the multifactorial nature of SLE and have demonstrated utility in improving diagnostic accuracy, classifying disease subtypes, and guiding personalized treatment strategies. This review offers a clinician-oriented overview of foundational ML approaches and their practical applications in SLE, highlighting how these tools might be incorporated into clinical workflows to support more timely, accurate, and individualized care.

Keywords

Systemic lupus erythematosus / machine learning / artificial intelligence

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