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

A. Guiga, A. Amara, M. Thabet, W. BenYahia, A. Baya Chatti, A. Atig. C and N. Ghannouchi
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A. Guiga: Department of Internal Medicine, Farhat Hached University Hospital, Faculty of Medicine, University of Sousse, 4002 Sousse, Tunisia
A. Amara: Department of Family and Community Medicine, Faculty of Medicine, University of Sousse, 4002 Sousse, Tunisia
M. Thabet: Department of Internal Medicine, Farhat Hached University Hospital, Faculty of Medicine, University of Sousse, 4002 Sousse, Tunisia
W. BenYahia: Department of Internal Medicine, Farhat Hached University Hospital, Faculty of Medicine, University of Sousse, 4002 Sousse, Tunisia
A. Baya Chatti: Department of Internal Medicine, Farhat Hached University Hospital, Faculty of Medicine, University of Sousse, 4002 Sousse, Tunisia
A. Atig. C: Department of Internal Medicine, Farhat Hached University Hospital, Faculty of Medicine, University of Sousse, 4002 Sousse, Tunisia
N. Ghannouchi: Department of Internal Medicine, Farhat Hached University Hospital, Faculty of Medicine, University of Sousse, 4002 Sousse, Tunisia

Journal of Innovations in Medical Research, 2025, vol. 4, issue 3, 34-37

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.

Keywords: Systemic Lupus Erythematosus; artificial intelligence; machine learning; biomarkers; personalized medicine; disease monitoring (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:bdz:joimer:v:4:y:2025:i:3:p:34-37

DOI: 10.63593/JIMR.2788-7022.2025.06.003

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