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Original article
Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations
  1. Joanna Kedra1,2,
  2. Timothy Radstake3,
  3. Aridaman Pandit3,
  4. Xenofon Baraliakos4,
  5. Francis Berenbaum5,
  6. Axel Finckh6,
  7. Bruno Fautrel1,2,
  8. Tanja A Stamm7,
  9. David Gomez-Cabrero8,
  10. Christian Pristipino9,
  11. Remy Choquet10,
  12. Hervé Servy11,
  13. Simon Stones12,
  14. Gerd Burmester13 and
  15. Laure Gossec1,2
  1. 1 Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), UMR S 1136, Sorbonne Universite, Paris, France
  2. 2 Rheumatology Department, Hôpital Universitaire Pitié Salpêtrière, APHP, Paris, France
  3. 3 Department of Rheumatology, Clinical Immunology and Laboratory for Translational Immunology, University of Utrecht Faculty of Medicine, Utrecht, The Netherlands
  4. 4 Herne, Ruhr-University, Rheumazentrum Ruhrgebiet, Bochum, Germany
  5. 5 Rheumatology Department, Hospital Saint-Antoine, APHP, Paris, Île-de-France, France
  6. 6 Division of Rheumatology, University Hospital of Geneva, Geneva, Switzerland
  7. 7 Section for Outcomes Research, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
  8. 8 Departamento de Salud-Universidad Pública de Navarra, Translational Bioinformatics Unit, Navarra Biomed, Pamplona, Spain
  9. 9 Interventional Cardiology Department, Ospedale San Filippo Neri, Rome, Italy
  10. 10 Orange e-Health, INSERM U1142, Paris, France
  11. 11 e-Health Services, Sanoïa, Gardanne, France
  12. 12 School of Healthcare, University of Leeds, Leeds, West Yorkshire, UK
  13. 13 Department of Rheumatology and Clinical Immunology, Charité - University Medicine Berlin, Berlin, Germany
  1. Correspondence to Dr Joanna Kedra;{at}


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.

  • big data
  • artificial intelligence
  • machine learning
  • biostatistics
  • rheumatology

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:

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  • Contributors All authors have provided data for the study, participated in the data interpretation and have approved the final version.

  • Funding Supported by the European League Against Rheumatism, EULAR (grant SCI018).

  • Competing interests RC is an employee of Orange Healthcare, and HS is an employee of Sanoïa, a Digital CRO providing clinical research services including data science. There are no competing interests for the other authors.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available upon reasonable request.

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