ReviewClinical rheumatology2025
Review of 2024 publications on the applications of artificial intelligence in rheumatology.
Review in Clinical rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Post-stroke rehabilitation in inflammatory rheumatic diseases: outcome measures, digital tools, and patient education perspectives.Rheumatology international · 2026Review
- Artificial Intelligence (AI) in rheumatology: a comparative evaluation of the ChatGPT and DeepSeek application.BMC rheumatology · 2026Article
- Review
- Artificial Intelligence in Rheumatology: Quo Vadis?Mediterranean journal of rheumatology · 2025Article
- Machine learning differentiation of rheumatoid arthritis-Sjögren's syndrome overlap from Sjögren's syndrome with polyarthritis.Frontiers in immunology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) into rheumatology has revolutionized research and clinical practice, offering transformative advancements in diagnostics, biomarker discovery, genomics, digital health technologies, and personalized medicine. This review provides a comprehensive analysis of cutting-edge AI applications in rheumatology, highlighting deep learning models for imaging diagnostics, AI-powered genomic analysis, and wearable health technologies for continuous disease monitoring. The findings demonstrate that AI enhances diagnostic precision, facilitates early disease detection, and enables personalized therapeutic strategies. However, significant challenges remain, including limited clinician adoption, ethical concerns, data privacy issues, and the need for robust model validation. A recent survey revealed that 73% of rheumatologists have never used AI in clinical practice, emphasizing the urgent need for targeted training and interdisciplinary collaboration. Additionally, AI is reshaping rheumatology research by optimizing drug discovery, clinical trial designs, and predictive analytics. Overcoming current barriers requires a multidisciplinary approach involving rheumatologists, AI specialists, and regulatory bodies to ensure the ethical, scalable, and effective implementation of AI-driven solutions in rheumatology.
Indexed as
Identifiers
40011358What 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.