Mini Review: Artificial Intelligence and Systemic Lupus Erythematosus (SLE)

Abstract

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease characterized by significant clinical heterogeneity, posing substantial challenges in diagnosis, monitoring, and treatment. Artificial Intelligence (AI), with its ability to analyze large and multidimensional datasets, offers innovative solutions to address these challenges. This review explores the current applications of AI in SLE research, highlighting its role in early diagnosis, biomarker discovery, imaging analysis, and personalized treatment strategies. We also discuss the integration of AI in disease monitoring, including the prediction of flares and remote patient management through telemedicine platforms. Despite its promise, the implementation of AI in SLE faces challenges such as data quality issues, ethical concerns, and the need for algorithm interpretability. Looking ahead, advancements in AI techniques, multi-omics integration, and interdisciplinary collaboration hold potential to overcome these barriers and transform SLE care. By synthesizing existing literature, this review underscores the transformative potential of AI in improving diagnostic accuracy, optimizing therapeutic interventions, and enhancing patient outcomes in SLE. Future research should focus on addressing current limitations and fostering equitable, clinically relevant AI applications to advance the field of lupus research and care.