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Review
. 2025 Apr 22:16:1525462.
doi: 10.3389/fimmu.2025.1525462. eCollection 2025.

Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades

Sidi Liu  1   2 , Yang Liu  3 , Ming Li  1   2 , Shuangshuang Shang  1   2 , Yunxiang Cao  1   2 , Xi Shen  1   2 , Chuanbing Huang  1   2
Affiliations
Review

Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades

Sidi Liu et al. Front Immunol. .

Abstract

Objective: Autoimmune diseases have long been recognized for their intricate nature and elusive mechanisms, presenting significant challenges in both diagnosis and treatment. The advent of artificial intelligence technology has opened up new possibilities for understanding, diagnosing, predicting, and managing autoimmune disorders. This study aims to explore the current state and emerging trends in the field through bibliometric analysis, providing guidance for future research directions.

Methods: The study employed the Web of Science Core Collection database for data acquisition and performed bibliometric analysis using CiteSpace, HistCite Pro, and VOSviewer.

Results: Over the past two decades, 1,695 publications emerged in this research field, including 1,409 research articles and 286 reviews. This investigation unveils the global development landscape predominantly led by the United States and China. The research identifies key institutions, such as Brigham & Women's Hospital, influential journals like the Annals of the Rheumatic Diseases, distinguished authors including Katherine P. Liao, and pivotal articles. It visually maps out the research clusters' evolutionary path over time and explores their applications in patient identification, risk factors, prognosis assessment, diagnosis, classification of disease subtypes, monitoring and decision support, and drug discovery.

Conclusion: AI is increasingly recognized for its potential in the field of autoimmune diseases, yet it continues to face numerous challenges, including insufficient model validation and difficulties in data integration and computational power. Significant advancements have been demanded to enhance diagnostic precision, improve treatment methodologies, and establish robust frameworks for data protection, thereby facilitating more effective management of these complex conditions.

Keywords: artificial intelligence; autoimmune diseases; bibliometric exploration; content analysis; forefront.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Analysis framework of this paper.
Figure 2
Figure 2
The evolution and distribution characteristics of publications. (A) The annual number of publications and citations over the past 20 years. (B) Top 20 Web of Science Categories for Publications. (C) Analysis of the publication numbers by country/region. (D) Global Distribution Overview of Publications.
Figure 3
Figure 3
The co-authorship network among countries/regions and academic institutions. (A) Co-authorship Network of Countries/Regions. (B) Publication Citation Network of Countries/Regions. (C) Co-authorship Network of academic institutions. (D) Publication Citation Network of academic institutions. Network nodes symbolize publication or citation values, with larger nodes signifying higher values; Links between nodes reflect collaboration or citation strength, where thicker and darker lines denote stronger connections; Nodes of the same color are part of the same cluster, indicating similar characteristic.
Figure 4
Figure 4
Author analysis of publications. (A) Network of co-authors of publications. (B) Citation network of authors in the field. (C) Co-citation network of authors. (D) Analysis of Total link strength and Normalized citations among the top 15 authors based on publication count.
Figure 5
Figure 5
Journals analysis of publications in research fields. (A) Citation network analysis of journals. (B) Analysis of Journal Citation Years in the Field. (C) Co-citation network analysis of journals. (D) The 25 Journals with the Highest Number of Publications. (E) Top 25 journals in terms of citations. (F) The dual-map overlay of journals.
Figure 6
Figure 6
Analysis of publications in the field of research. (A) Network diagram of literatures citation analysis. (B) Network diagram for co-citation analysis of literatures. Node size indicates the citation count of the literature. The same color represents the same cluster.
Figure 7
Figure 7
Analysis of the development characteristics of literature clusters. (A) Literature clustering analysis and its evolutionary trajectory. (B) Top 25 References with the Strongest Citation Bursts.
Figure 8
Figure 8
Keywords network analysis in the field. (A) Keyword co-occurrence network analysis. (B) Analysis of Keywords co-occurrence networks in a temporal perspective. (C) Top 30 Keywords with the Strongest Citation Bursts.
Figure 9
Figure 9
Keywords clustering analysis and development trajectory. (A) Keyword-based cluster analysis. (B) Landscape map analysis of keyword clusters.
Figure 10
Figure 10
The scheme of major applications of artificial intelligence technology in autoimmune diseases.
See this image and copyright information in PMC

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