ArticleAnnals of the rheumatic diseases2020
EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases.
Article in Annals of the rheumatic diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 6 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 6 syntheses or guidelines pooled it, 82 citations in OpenAlex.
- Ethical issues raised by artificial intelligence and big data in population health: a scoping review.Frontiers in sociology · 2025Pooled it
- Digitalization and disruptive change in rheumatology.Zeitschrift fur Rheumatologie · 2023Pooled it
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- Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature.Frontiers in immunology · 2022Pooled it
- Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.RMD open · 2019Pooled it
- Reporting of Research Ethics in Studies Focusing on Foot Health in Patients with Rheumatoid Arthritis - A Systematic Review.Journal of empirical research on human research ethics : JERHREPooled it
- Efficacy of Risankizumab across distinct PsA phenotypes identified with machine learning analytics using data from biologic DMARD-Naïve patients in two phase 3 clinical trials.Arthritis research & therapy · 2025Trial
- Digital self-management engagement and long-term patient-reported outcome trajectories in inflammatory arthritis: a real-world retrospective cohort study.Clinical rheumatology · 2026Article
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
- Artificial intelligence in rheumatology and paediatric rheumatology: insights from an international survey by EMEUNET.EULAR rheumatology open · 2026Article
- Ultrasonographic Evaluation of Asymptomatic Shoulder Involvement in Rheumatoid Arthritis: A Prospective Case-Control Study.Journal of clinical medicine · 2026Article
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- 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
- Rheumatoid arthritis patients' perspective on the use of prediction models in clinical decision-making.Rheumatology (Oxford, England) · 2025Article
- Achieving health equity in immune disease: leveraging big data and artificial intelligence in an evolving health system landscape.Frontiers in big data · 2025Review
- Article
- The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence.Rheumatology international · 2024Review
- Vignette-based comparative analysis of ChatGPT and specialist treatment decisions for rheumatic patients: results of the Rheum2Guide study.Rheumatology international · 2024Article
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Entry point of machine learning in axial spondyloarthritis.RMD open · 2024Review
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Authors and funding
14 authors at 9 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs).
methodsA multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated.
resultsThree overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice.
conclusionThese EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.
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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.