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Artificial Intelligence in medical imaging practice: looking to the future
Artificial intelligence (AI) is heralded as the most disruptive technology to health services in the 21(st) century. Many commentary articles published in the general public and health domains recognise that medical imaging is at the forefront of these changes due to our large digital data footprint...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920680/ https://www.ncbi.nlm.nih.gov/pubmed/31709775 http://dx.doi.org/10.1002/jmrs.369 |
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author | Lewis, Sarah J Gandomkar, Ziba Brennan, Patrick C |
author_facet | Lewis, Sarah J Gandomkar, Ziba Brennan, Patrick C |
author_sort | Lewis, Sarah J |
collection | PubMed |
description | Artificial intelligence (AI) is heralded as the most disruptive technology to health services in the 21(st) century. Many commentary articles published in the general public and health domains recognise that medical imaging is at the forefront of these changes due to our large digital data footprint. Radiomics is transforming medical images into mineable high‐dimensional data to optimise clinical decision‐making; however, some would argue that AI could infiltrate workplaces with very few ethical checks and balances. In this commentary article, we describe how AI is beginning to change medical imaging services and the innovations that are on the horizon. We explore how AI and its various forms, including machine learning, will challenge the way medical imaging is delivered from workflow, image acquisition, image registration to interpretation. Diagnostic radiographers will need to learn to work alongside our ‘virtual colleagues’, and we argue that there are vital changes to entry and advanced curricula together with national professional capabilities to ensure machine‐learning tools are used in the safest and most effective manner for our patients. |
format | Online Article Text |
id | pubmed-6920680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69206802019-12-30 Artificial Intelligence in medical imaging practice: looking to the future Lewis, Sarah J Gandomkar, Ziba Brennan, Patrick C J Med Radiat Sci Commentary Artificial intelligence (AI) is heralded as the most disruptive technology to health services in the 21(st) century. Many commentary articles published in the general public and health domains recognise that medical imaging is at the forefront of these changes due to our large digital data footprint. Radiomics is transforming medical images into mineable high‐dimensional data to optimise clinical decision‐making; however, some would argue that AI could infiltrate workplaces with very few ethical checks and balances. In this commentary article, we describe how AI is beginning to change medical imaging services and the innovations that are on the horizon. We explore how AI and its various forms, including machine learning, will challenge the way medical imaging is delivered from workflow, image acquisition, image registration to interpretation. Diagnostic radiographers will need to learn to work alongside our ‘virtual colleagues’, and we argue that there are vital changes to entry and advanced curricula together with national professional capabilities to ensure machine‐learning tools are used in the safest and most effective manner for our patients. John Wiley and Sons Inc. 2019-11-10 2019-12 /pmc/articles/PMC6920680/ /pubmed/31709775 http://dx.doi.org/10.1002/jmrs.369 Text en © 2019 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Commentary Lewis, Sarah J Gandomkar, Ziba Brennan, Patrick C Artificial Intelligence in medical imaging practice: looking to the future |
title | Artificial Intelligence in medical imaging practice: looking to the future |
title_full | Artificial Intelligence in medical imaging practice: looking to the future |
title_fullStr | Artificial Intelligence in medical imaging practice: looking to the future |
title_full_unstemmed | Artificial Intelligence in medical imaging practice: looking to the future |
title_short | Artificial Intelligence in medical imaging practice: looking to the future |
title_sort | artificial intelligence in medical imaging practice: looking to the future |
topic | Commentary |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920680/ https://www.ncbi.nlm.nih.gov/pubmed/31709775 http://dx.doi.org/10.1002/jmrs.369 |
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