Artificial intelligence in rheumatology and paediatric rheumatology: insights from an international survey by EMEUNET
Year: 2026
Type: article
Study design: Observational Study
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.
Source: EULAR Rheumatology Open
Authors: Saverio La Bella, Ana Isabel Rebollo-Giménez, Krystel Aouad, Latika Gupta, Tue Kragstrup, Deniz Bayraktar, Thomas Davergne, Davide Cangelosi, Thomas Hügle, Johannes Knitza, Nicolino Ruperto, Vincenzo Venerito, Wilson Bautista-Molano, Dzifa Dey, Abdellah El Maghraoui, Ihsane Hmamouchi, Alison M. Hoens, Linda C. Li, Angela Migowa, Erin Treemarcki, Nelly Ziade, Alessandra Alongi, Diego Benavent
Institutions: Paediatric Rheumatology INternational Trials Organization (PRINTO), University of Milano-Bicocca, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Saint George Hospital, The Francis Crick Institute, The Royal Wolverhampton NHS Trust, University of Birmingham, Aarhus University, Aarhus University Hospital, Regionshospitalet Silkeborg, Central Denmark Region, University of Iceland, Izmir Kâtip Çelebi University, Inserm, Université Paris Cité, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Université Sorbonne Paris Nord, Centre de Recherche Épidémiologie et Statistique, Istituto Giannina Gaslini, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Philipps University of Marburg, University of Bari Aldo Moro, Universidad El Bosque, Fundación Santa Fe de Bogotá, University of Ghana, Korle Bu Teaching Hospital, Mohammed V University, International University of Rabat, University of British Columbia, Aga Khan University Nairobi, University of Utah, University of Utah Health Care, Saint Joseph University, Hôtel-Dieu de France, Bellvitge University Hospital
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,080
OpenAlex ID: W7148630051