AI News, Current Issue Table of Contents: European Journal of Radiology artificial intelligence

Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations | RMD Open

First defined as data sets too large or complex for traditional analysis methods,11 this concept has evolved and the ‘5 V’ paradigm (for volume, velocity, veracity, variety and value) is more and more used.26–28 The definition provided in recent EMA recommendations may be considered as a synthesis of all these notions.12 Although all the authors of the selected articles agree that big data refers to a very large number of data points, there is also no consensual ‘cut-off’ to define what is meant by ‘very large’.

Some authors proposed log(n×p) superior or equal to 7 (n being the number of units of observation and p the number of variables),29 however, giving the rapid growth of datasets in the last decade, some authors rather propose to think in terms of terabytes (1012) or petabytes (1015).9 10 Nevertheless, even terabytes and petabytes will be soon too restrictive, since according to an International Data Corporation report prediction, the global data volume will grow exponentially from 4.4 zettabytes to 44 zettabytes (1021) between 2013 and 2020.30 This issue shows that the definition of big data is beyond the scope of the characteristics of data type and cannot be restricted to the size or volume of those data.31 It confirms also the disparity in the number of data reported in the studies.

With the increasing amount of information collected by registries, Electronic Health Records and the increasing use of sensors collecting in real time patients’ data, clinical research must evolve to take advantage of these new sources of information and implement them in routine practice.2 32 Given the exhaustive nature of clinical big data, they could be particularly interesting in the future to study rare diseases, rare outcomes and evaluate the efficacy of treatments in non-selected populations, which are difficult to assess in usual clinical trials.2 33 Omics is a growing field, particularly promising for personalised medicine as it supports the discovery of predictive biomarkers and therapeutic targets.3 4 34 Imaging is also a very interesting application of big data for diagnosis and clinical decision making.

Since any single radiological exam compiles a huge amount of data, medical imaging is particularly conducive to the use of AI and notably machine learning methods.35 36 Examples of applications of big data in medical imaging are numerous and varied, from diagnosis and follow-up of cancers,37 38 to scoliosis39 or diabetic retinopathy.40 As social networks and Internet-driven data are exponentially growing, text mining is becoming a relevant source for health information: recent applications were the prediction of influenza and pertussis epidemics thanks to Google searches41 42 and prediction of depression thanks to Facebook statuses.43 In the field of RMDs, only one paper included in the SLR was based on Google, Wikipedia and Youtube searches concerning inflammatory vasculitis,44 contrasting with the variety of examples provided in other medical fields.

EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases | Annals of the Rheumatic Diseases

It is reassuring that our proposals were not in contradiction to other recent recommendations, such as those of the EMA or the National Health Service in the UK.17 18 To our knowledge, no other non-governmental organisation representing patients, healthcare professional and scientific societies to date has developed recommendations for big data.

While the American College of Rheumatology has not published specific guidance relating to big data, it has developed an online patient registry from electronic health records which could potentially be used as a big data source.71 The use of big data is rapidly expending as witnessed by the increasing number of organisations, companies and publications/books dealing with this topic.

With the growth of big data in RMDs, we expect that these PTC inspire governmental and research organisations, healthcare providers, researchers and patients to increase relevant training of the stakeholders, promotes research on interpretation and clinical applications of big data results, and develop benchmarks/guidelines for reproducible research.

Points 8 and 9 referring to validation and implementation raised much debate within the task force since we felt it was important to both insist on the importance of these steps and at the same time aim for applicability/feasibility of the points to consider.

The grading of the evidence was a challenge in the present work as the Oxford level of evidence27 which is used in EULAR task forces is better adapted to therapeutic evidence than to observational or prognostic evidence as is often obtained in big data work.

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