Promises of artificial intelligence in neuroradiology: a systematic technographic review

Allard W Olthof*, Peter M A van Ooijen, Mohammad H Rezazade Mehrizi

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)
42 Downloads (Pure)

Abstract

Purpose To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists. Methods We identified AI applications offered on the market during the period 2017-2019. We systematically collected and structured information in a relational database and coded for the characteristics of the applications, their functionalities for the radiology workflow and their potential impacts in terms of 'supporting', 'extending' and 'replacing' radiology tasks. Results We identified 37 AI applications in the domain of neuroradiology from 27 vendors, together offering 111 functionalities. The majority of functionalities 'support' radiologists, especially for the detection and interpretation of image findings. The second-largest group of functionalities 'extends' the possibilities of radiologists by providing quantitative information about pathological findings. A small but noticeable portion of functionalities seek to 'replace' certain radiology tasks. Conclusion Artificial intelligence in neuroradiology is not only in the stage of development and testing but also available for clinical practice. The majority of functionalities support radiologists or extend their tasks. None of the applications can replace the entire radiology profession, but a few applications can do so for a limited set of tasks. Scientific validation of the AI products is more limited than the regulatory approval.

Original languageEnglish
Pages (from-to)1265-1278
Number of pages14
JournalNeuroradiology
Volume62
Issue number10
DOIs
Publication statusPublished - 22-Apr-2020

Keywords

  • Artificial intelligence (AI)
  • Machine learning
  • Organizational innovation
  • Neurology
  • diagnostic imaging
  • Radiology
  • Technography
  • RADIOLOGY
  • PERFORMANCE
  • WORKING
  • GUIDE
  • NEED

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