SynthesisRMD open2019
Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.
Synthesis in RMD open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
- Predicting anti-CCP positivity and early rheumatoid arthritis onset from routine laboratory parameters: a SHAP-explained machine learning pipeline.Frontiers in medicine · 2026Article
- Artificial intelligence in rheumatology: status quo and quo vadis-results of a national survey among German rheumatologists.Therapeutic advances in musculoskeletal disease · 2024Article
- ChatGPT: when artificial intelligence replaces the rheumatologist in medical writing.Annals of the rheumatic diseases · 2023Article
- Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review.Frontiers in medicine · 2023Review
- Editorial: Global excellence in rheumatology: Europe.Frontiers in medicine · 2023Article
- Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews.International journal of medical informatics · 2022Review
- The Digital Way to Intercept Psoriatic Arthritis.Frontiers in medicine · 2021Review
- Review
- Digital health technologies: opportunities and challenges in rheumatology.Nature reviews. Rheumatology · 2020Review
- Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?Sensors (Basel, Switzerland) · 2020Review
- Big data and data processing in rheumatology: bioethical perspectives.Clinical rheumatology · 2020Review
- Machine learning in rheumatology approaches the clinic.Nature reviews. Rheumatology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs). Methods: A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs. Results: Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000-5 billion) in RMDs, and 9.1 billion (range 100 000-200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs). Conclusions: Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.
Indexed as
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.