ArticleEULAR rheumatology open2026
Artificial intelligence in rheumatology and paediatric rheumatology: insights from an international survey by EMEUNET.
Article in EULAR rheumatology open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Perceptions, knowledge and adoption of artificial intelligence in rheumatology: results from a British Society for Rheumatology survey.Rheumatology (Oxford, England) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Artificial intelligence (AI) is revolutionising medicine. The aim of this study was to detail its use, opinions, knowledge, and concerns in rheumatology and paediatric rheumatology. Methods: A web-based survey open to all professionals working in the field was developed by the Emerging EULAR Network (EMEUNET) and disseminated between March and July 2025 in collaboration with other international rheumatology societies (AFLAR, ArLAR, CARRA, PAFLAR, PANLAR). The survey was divided into 4 sections: (i) participants' characteristics, (ii) AI use and applications, (iii) opinions and knowledge, and (iv) concerns, needs, and expectations. Results: Overall, 461 responses were collected from 59 countries. Respondents were mostly physicians who completed their training (316, 68.7%) and were based in Europe (170, 36.9%). Most participants (397, 86.7%) used AI for medical purposes, especially large language models (385, 83.7%) for grammar correction and brainstorming. Although there was broad optimism about its use (366, 79.6%), self-reported practical skills were predominantly basic or still in development (346, 75.1%), and knowledge was rarely defined as strong or expert-level (63, 13.7%). Concerns focused on ethics (314, 69%), lack of trust (316, 69.5%), and insufficient training (270, 59.3%). Disparities emerged across geographic regions in use, knowledge, and practical skills. Conclusions: AI is widely used and positively perceived in rheumatology, despite limited knowledge and practical skills, and regional disparities. Addressing gaps in ethics, transparency, and insufficient training through targeted education and implementation strategies will be essential to ensure an equitable and effective integration into clinical and research practice.
Identifiers
What Socratic 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 Socratic graph.