What question did this study set out to answer?

The aim is to evaluate the current applications of artificial intelligence in systemic lupus erythematosus and identify barriers to clinical translation.

May 22, 2026

Artificial intelligence in the care of systemic lupus erythematosus: Current capabilities and translational barriers

Key Points

The aim is to evaluate the current applications of artificial intelligence in systemic lupus erythematosus and identify barriers to clinical translation.
Critical appraisal of evidence for AI applications across the SLE clinical workflow.
Evaluation criteria included external validation, prospective testing, algorithmic fairness, and regulatory status.
Review of AI diagnostic and monitoring tools, predictive models, and treatment personalization strategies.
Diagnostic tools include SLERPI index with multinational validation and LSTM flare-prediction model (C-index 0.897).
Serum IgA2 anti-dsDNA is identified as a validated SLE response biomarker across independent trials.
No AI tool has received regulatory clearance, and populations most affected by SLE are underrepresented in training data.

Abstract

Background The clinical heterogeneity of systemic lupus erythematosus exceeds the resolution of conventional disease activity instruments. Artificial intelligence offers the analytical infrastructure to engage with this complexity, yet the translation of AI models into clinical practice remains limited. Methods This review critically appraises the current evidence for AI applications across the SLE clinical workflow, including computational phenotyping, diagnosis, disease activity monitoring, organ-specific predictive modelling, and treatment personalization. Studies were evaluated for external validation, prospective testing, algorithmic fairness, explainability, and regulatory status. Findings AI applications across the SLE clinical workflow show uneven methodological maturity. Diagnostic and monitoring tools include the SLERPI index, with multinational external validation, and an LSTM flare-prediction model (C-index 0.897). Treatment personalization is anchored by serum IgA2 anti-dsDNA, the only SLE response biomarker validated across independent trials, and by multi-stain deep learning on renal biopsies (AUC 0.84, three external centers). Predictive modelling for organ-specific manifestations has progressed unevenly: cardiovascular risk stratification (SLECRISK) and a multicenter thrombocytopenia model are the most mature, while neuropsychiatric, gastrointestinal, and ocular applications remain single-center proofs of concept. External validation is the exception across SLE prediction models, no AI tool has received regulatory clearance, and populations most affected by SLE remain underrepresented in training data. Conclusions AI demonstrates genuine analytical capability in SLE but the translation gap is defined by insufficient validation, limited explainability, and absent equity evidence. Closing it will require fewer published models and more validated tools, optimization for clinical impact rather than discrimination alone, and demonstration of performance equity across the demographic spectrum of the disease.

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