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Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review
RATIONAL: Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool. However, current practice shows low deployment of these algorithms in clinical practice, because DL algor...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213317/ https://www.ncbi.nlm.nih.gov/pubmed/37250654 http://dx.doi.org/10.3389/fmed.2023.1180773 |
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author | de Vries, Bart M. Zwezerijnen, Gerben J. C. Burchell, George L. van Velden, Floris H. P. Menke-van der Houven van Oordt, Catharina Willemien Boellaard, Ronald |
author_facet | de Vries, Bart M. Zwezerijnen, Gerben J. C. Burchell, George L. van Velden, Floris H. P. Menke-van der Houven van Oordt, Catharina Willemien Boellaard, Ronald |
author_sort | de Vries, Bart M. |
collection | PubMed |
description | RATIONAL: Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool. However, current practice shows low deployment of these algorithms in clinical practice, because DL algorithms lack transparency and trust due to their underlying black-box mechanism. For successful employment, explainable artificial intelligence (XAI) could be introduced to close the gap between the medical professionals and the DL algorithms. In this literature review, XAI methods available for magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET) imaging are discussed and future suggestions are made. METHODS: PubMed, Embase.com and Clarivate Analytics/Web of Science Core Collection were screened. Articles were considered eligible for inclusion if XAI was used (and well described) to describe the behavior of a DL model used in MR, CT and PET imaging. RESULTS: A total of 75 articles were included of which 54 and 17 articles described post and ad hoc XAI methods, respectively, and 4 articles described both XAI methods. Major variations in performance is seen between the methods. Overall, post hoc XAI lacks the ability to provide class-discriminative and target-specific explanation. Ad hoc XAI seems to tackle this because of its intrinsic ability to explain. However, quality control of the XAI methods is rarely applied and therefore systematic comparison between the methods is difficult. CONCLUSION: There is currently no clear consensus on how XAI should be deployed in order to close the gap between medical professionals and DL algorithms for clinical implementation. We advocate for systematic technical and clinical quality assessment of XAI methods. Also, to ensure end-to-end unbiased and safe integration of XAI in clinical workflow, (anatomical) data minimization and quality control methods should be included. |
format | Online Article Text |
id | pubmed-10213317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102133172023-05-27 Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review de Vries, Bart M. Zwezerijnen, Gerben J. C. Burchell, George L. van Velden, Floris H. P. Menke-van der Houven van Oordt, Catharina Willemien Boellaard, Ronald Front Med (Lausanne) Medicine RATIONAL: Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool. However, current practice shows low deployment of these algorithms in clinical practice, because DL algorithms lack transparency and trust due to their underlying black-box mechanism. For successful employment, explainable artificial intelligence (XAI) could be introduced to close the gap between the medical professionals and the DL algorithms. In this literature review, XAI methods available for magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET) imaging are discussed and future suggestions are made. METHODS: PubMed, Embase.com and Clarivate Analytics/Web of Science Core Collection were screened. Articles were considered eligible for inclusion if XAI was used (and well described) to describe the behavior of a DL model used in MR, CT and PET imaging. RESULTS: A total of 75 articles were included of which 54 and 17 articles described post and ad hoc XAI methods, respectively, and 4 articles described both XAI methods. Major variations in performance is seen between the methods. Overall, post hoc XAI lacks the ability to provide class-discriminative and target-specific explanation. Ad hoc XAI seems to tackle this because of its intrinsic ability to explain. However, quality control of the XAI methods is rarely applied and therefore systematic comparison between the methods is difficult. CONCLUSION: There is currently no clear consensus on how XAI should be deployed in order to close the gap between medical professionals and DL algorithms for clinical implementation. We advocate for systematic technical and clinical quality assessment of XAI methods. Also, to ensure end-to-end unbiased and safe integration of XAI in clinical workflow, (anatomical) data minimization and quality control methods should be included. Frontiers Media S.A. 2023-05-12 /pmc/articles/PMC10213317/ /pubmed/37250654 http://dx.doi.org/10.3389/fmed.2023.1180773 Text en Copyright © 2023 de Vries, Zwezerijnen, Burchell, van Velden, Menke-van der Houven van Oordt and Boellaard. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine de Vries, Bart M. Zwezerijnen, Gerben J. C. Burchell, George L. van Velden, Floris H. P. Menke-van der Houven van Oordt, Catharina Willemien Boellaard, Ronald Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review |
title | Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review |
title_full | Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review |
title_fullStr | Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review |
title_full_unstemmed | Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review |
title_short | Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review |
title_sort | explainable artificial intelligence (xai) in radiology and nuclear medicine: a literature review |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213317/ https://www.ncbi.nlm.nih.gov/pubmed/37250654 http://dx.doi.org/10.3389/fmed.2023.1180773 |
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