ReviewRheumatology advances in practice2025
Large language models and rheumatology: are we there yet?
Review in Rheumatology advances in practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Bridging the Precision Gap in Rheumatoid Arthritis: Spatial Transcriptomics, Spatial Proteomics, and Artificial Intelligence in Precision Health.Biomedicines · 2026Review
- Low-energy small language models with retrieval-augmented generation can surpass large-model performance in rheumatology.Frontiers in medicine · 2026Article
- ChatGPT, Gemini, and Grok on familial mediterranean fever: are they trustworthy?Clinical rheumatology · 2026Article
- Optimising the clinical application of rheumatology guidelines using large language models: a retrieval-augmented generation framework integrating EULAR and ACR recommendations.EULAR rheumatology open · 2025Article
- From chat to act: large language model agents and agentic AI as the next frontier of AI in rheumatology.EULAR rheumatology open · 2025Review
- Artificial Intelligence in Rheumatology: Quo Vadis?Mediterranean journal of rheumatology · 2025Article
- Forecasting Achievement of Inactive Disease in Juvenile Idiopathic Arthritis with Artificial Intelligence.Children (Basel, Switzerland) · 2025Article
- AI in psoriatic disease ("PsAI"): Current insights and future directions.Clinical rheumatology · 2025Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Artificial intelligence in rheumatology: days of a future past.Rheumatology advances in practice · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The last 2 years have marked the beginning of a golden age for natural language processing in medicine. The arrival of large language models (LLMs) and multimodal models have raised new opportunities and challenges for research and clinical practice. In rheumatology, a specialty rich in data and requiring complex decision-making, the use of these tools may transform diagnostic procedures, improve patient interaction and simplify data management, leading to more personalized and efficient healthcare outcomes. The objective of this article is to present an overview of the status of LLMs in the field of rheumatology while discussing some of the challenges ahead in this area of great potential.
Indexed as
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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.