TY - JOUR
T1 - Current status of use of big data and artificial intelligence in RMDs
T2 - a systematic literature review informing EULAR recommendations
AU - Kedra, Joanna
AU - Radstake, Timothy
AU - Pandit, Aridaman
AU - Baraliakos, Xenofon
AU - Berenbaum, Francis
AU - Finckh, Axel
AU - Fautrel, Bruno
AU - Stamm, Tanja A
AU - Gomez-Cabrero, David
AU - Pristipino, Christian
AU - Choquet, Remy
AU - Servy, Hervé
AU - Stones, Simon
AU - Burmester, Gerd
AU - Gossec, Laure
N1 - Funding Information:
Supported by the European League Against Rheumatism, EULAR (grant SCI018).
Publisher Copyright:
© 2019 Author(s) (or their employer(s)). Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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.
KW - Advisory Committees/organization & administration
KW - Artificial Intelligence/trends
KW - Big Data
KW - Europe/epidemiology
KW - Humans
KW - Information Storage and Retrieval/trends
KW - Machine Learning/statistics & numerical data
KW - Musculoskeletal Diseases/epidemiology
KW - Neural Networks, Computer
KW - Publications/trends
KW - Radiology/trends
KW - Rheumatic Diseases/epidemiology
KW - Sensitivity and Specificity
KW - biostatistics
KW - rheumatology
KW - machine learning
KW - artificial intelligence
KW - big data
UR - http://www.scopus.com/inward/record.url?scp=85073894517&partnerID=8YFLogxK
U2 - 10.1136/rmdopen-2019-001004
DO - 10.1136/rmdopen-2019-001004
M3 - Review article
C2 - 31413871
SN - 2056-5933
VL - 5
JO - RMD Open
JF - RMD Open
IS - 2
M1 - e001004
ER -