SynthesisFrontiers in medicine2026
The application of artificial intelligence in systemic lupus erythematosus: a bibliometric analysis of current trends and future directions.
Synthesis in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Systemic lupus erythematosus (SLE) poses significant clinical challenges due to its heterogeneity and unpredictable relapses. Artificial intelligence (AI) is driving a paradigm shift in SLE management through molecular subtyping and prognostic modeling. However, a comprehensive quantitative analysis of this rapidly growing field is lacking. This study employs bibliometric and visualization methods to systematically map the development, collaboration patterns, and research frontiers of AI in SLE. Methods: English-language original articles and reviews on AI in SLE, published between January 1, 2005, and June 10, 2026, were retrieved from the Web of Science Core Collection and Scopus. After rigorous screening, 707 core publications were analyzed using R-Bibliometrix, VOSviewer, and CiteSpace to evaluate publication trends, collaboration networks, keyword co-occurrence, and citation bursts. Results: The 707 included publications demonstrated a polynomial accelerated growth trend since 2005. Research has evolved from algorithmic proof-of-concepts to deep learning and multi-omics integration. China and the United States lead global output and collaboration, with institutions like the Karolinska Institute showing significant impact. Although "machine learning" and "lupus nephritis" remain core topics, the research focus has expanded. Current studies increasingly emphasize molecular stratification, automated pathological image classification, and the longitudinal prediction of flares and organ damage. Recent citation bursts for "immunosuppressive agent," "tacrolimus," and "prednisone" highlight personalized treatment efficacy prediction as the latest frontier. Conclusion: AI has matured from methodological exploration into a robust clinical decision-support tool for SLE, particularly for lupus nephritis assessment and flare prediction. Future advancements rely on conducting prospective clinical validations in real-world cohorts, predicting personalized drug efficacies, and leveraging multi-omics to decode pathological mechanisms. These steps are essential to transition SLE management from traditional empirical approaches to data-driven precision medicine.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.