ReviewEULAR rheumatology open2025
From chat to act: large language model agents and agentic AI as the next frontier of AI in rheumatology.
Review in EULAR rheumatology open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Agentic AI and Large Language Models in Radiology: Opportunities and Hallucination Challenges.Bioengineering (Basel, Switzerland) · 2025Review
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Large language models (LLMs) have begun to influence rheumatology, yet their static knowledge and hallucination risks limit their potential. Retrieval-augmented generation mitigates some limitations, but complex rheumatologic care demands real-time data access, multistep reasoning, and tool usage that exceed standard LLM capabilities. The objective of this study is to explore how agentic artificial intelligence (AI) can address the limitations of current LLM applications in rheumatology. Methods: We conducted a viewpoint analysis of the capabilities of agentic AI systems, focusing on their technical foundations, current use cases in healthcare, and relevance to the specific demands of rheumatologic care. Results: Agentic AI extends LLMs with planning, memory, and the ability to interact with external tools, enabling execution of complex tasks. These capabilities offer promising applications in rheumatology, including personalized treatment planning, automated literature synthesis, and clinical decision support. Conclusions: Agentic AI systems represent a necessary evolution to meet the complexity of rheumatologic care. Regulatory, ethical and technical challenges must be overcome before agentic systems can be safely deployed in routine rheumatologic care.
Identifiers
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.