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Promises of artificial intelligence in neuroradiology: a systematic technographic review

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...

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Autores principales: Olthof, Allard W., van Ooijen, Peter M.A., Rezazade Mehrizi, Mohammad H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479016/
https://www.ncbi.nlm.nih.gov/pubmed/32318774
http://dx.doi.org/10.1007/s00234-020-02424-w
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author Olthof, Allard W.
van Ooijen, Peter M.A.
Rezazade Mehrizi, Mohammad H.
author_facet Olthof, Allard W.
van Ooijen, Peter M.A.
Rezazade Mehrizi, Mohammad H.
author_sort Olthof, Allard W.
collection PubMed
description 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.
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spelling pubmed-74790162020-09-21 Promises of artificial intelligence in neuroradiology: a systematic technographic review Olthof, Allard W. van Ooijen, Peter M.A. Rezazade Mehrizi, Mohammad H. Neuroradiology Diagnostic Neuroradiology 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. Springer Berlin Heidelberg 2020-04-22 2020 /pmc/articles/PMC7479016/ /pubmed/32318774 http://dx.doi.org/10.1007/s00234-020-02424-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Diagnostic Neuroradiology
Olthof, Allard W.
van Ooijen, Peter M.A.
Rezazade Mehrizi, Mohammad H.
Promises of artificial intelligence in neuroradiology: a systematic technographic review
title Promises of artificial intelligence in neuroradiology: a systematic technographic review
title_full Promises of artificial intelligence in neuroradiology: a systematic technographic review
title_fullStr Promises of artificial intelligence in neuroradiology: a systematic technographic review
title_full_unstemmed Promises of artificial intelligence in neuroradiology: a systematic technographic review
title_short Promises of artificial intelligence in neuroradiology: a systematic technographic review
title_sort promises of artificial intelligence in neuroradiology: a systematic technographic review
topic Diagnostic Neuroradiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479016/
https://www.ncbi.nlm.nih.gov/pubmed/32318774
http://dx.doi.org/10.1007/s00234-020-02424-w
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