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Review
Use of artificial intelligence in imaging in rheumatology – current status and future perspectives
  1. Berend Stoel
  1. Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands
  1. Correspondence to Dr Berend Stoel; B.C.Stoel{at}lumc.nl

Abstract

After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock wave that probably exceeds the impact of the first AI victory of defeating the world chess champion in 1997, some reflection may be appropriate on the consequences for clinical imaging in rheumatology. In this narrative review, a short explanation is given about the various AI techniques, including ‘deep learning’, and how these have been applied to rheumatological imaging, focussing on rheumatoid arthritis and systemic sclerosis as examples. By discussing the principle limitations of AI and deep learning, this review aims to give insight into possible future perspectives of AI applications in rheumatology.

  • magnetic resonance imaging
  • outcomes research
  • rheumatoid arthritis
  • systemic sclerosis
http://creativecommons.org/licenses/by-nc/4.0/

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: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Contributors BCS performed the literature search and wrote the manuscript.

  • Funding Parts of the presented illustrations originate from the APPEAR project and ESMIRA project, both funded by the Dutch Technology Foundation (currently TTW) as part of the Netherlands Organization for Scientific Research (NWO), under grant number LPG. 07998 and 13329, respectively, and from support by China Scholarship Council scholarship number 201406120046.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Commissioned; externally peer reviewed.

  • Data availability statement Not applicable

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