Promises of artificial intelligence in neuroradiology: a systematic technographic review
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DIAGNOSTIC NEURORADIOLOGY
Promises of artificial intelligence in neuroradiology: a systematic technographic review Allard W. Olthof 1,2 & Peter M.A. van Ooijen 2,3 & Mohammad H. Rezazade Mehrizi 4 Received: 6 January 2020 / Accepted: 27 March 2020 # The Author(s) 2020
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. Keywords Artificial intelligence (AI) . Machine learning . Organizational innovation . Neurology/diagnostic imaging . Radiology . Technography
Introduction Currently, artificial intelligence (AI) is a significant yet emerging technological innovation in healthcare. AI
* Allard W. Olthof [email protected] 1
Department of Radiology, Treant Health Care Group, Dr. G.H. Amshoffweg 1, Hoogeveen, The Netherlands
2
Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, The Netherlands
3
Data Science Center in Health (DASH), Machine Learning Lab, University of Groningen, University Medical Center Groningen, Zielstraweg 2, Groningen, The Netherlands
4
School of Business and Economics, Knowledge, Information and Innovation, KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, The Netherlands
represents technologies that involve developing machines that can perform tasks that are characteristic of human intelligence [1]. Neuroradiology is one of the leading subspecialties in radiology in terms of the diversity and number of AI applications [2, 3]. Examples include the automated id
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