Artificial intelligence in rheumatology: a cross-sectional Scopus-based analysis.

Area of Science:

• Rheumatology
• Artificial Intelligence
• Bibliometrics

Background:

• Artificial intelligence (AI) applications are growing in clinical medicine.
• Rheumatology is a suitable field for AI due to disease complexity and multisystemic nature.

Purpose of the Study:

• To conduct a bibliometric analysis of global research on artificial intelligence in rheumatology.
• To identify trends, key contributors, and research gaps in AI-driven rheumatology.

Main Methods:

• Bibliometric analysis of Scopus database using "artificial intelligence" AND "rheum*" keywords.
• Data extraction included publication year, country, institution, author, journal, and keywords.
• Linear regression analyzed temporal trends; disease-specific and survey-based studies were identified.

Main Results:

• A total of 1,057 publications were identified, with a sharp rise in output after 2018, peaking in 2025.
• The United States led in publications, followed by the UK and India.
• Osteoarthritis and rheumatoid arthritis were the most researched diseases, while others like systemic vasculitis were underrepresented.

Conclusions:

• AI research in rheumatology shows significant growth but is concentrated in specific countries, institutions, and disease areas.
• The rise in survey-based studies suggests increasing focus on clinical and patient perspectives.
• Future research should promote international collaboration, address research gaps in underrepresented diseases, and enhance ethical standards for AI adoption.