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Molecular-based precision oncology clinical decision making augmented by artificial intelligence

The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends...

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Detalles Bibliográficos
Autores principales: Zeng, Jia, Shufean, Md Abu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Portland Press Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786281/
https://www.ncbi.nlm.nih.gov/pubmed/34874054
http://dx.doi.org/10.1042/ETLS20210220
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author Zeng, Jia
Shufean, Md Abu
author_facet Zeng, Jia
Shufean, Md Abu
author_sort Zeng, Jia
collection PubMed
description The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians’ decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.
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spelling pubmed-87862812022-02-01 Molecular-based precision oncology clinical decision making augmented by artificial intelligence Zeng, Jia Shufean, Md Abu Emerg Top Life Sci Review Articles The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians’ decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed. Portland Press Ltd. 2021-12-21 2021-12-07 /pmc/articles/PMC8786281/ /pubmed/34874054 http://dx.doi.org/10.1042/ETLS20210220 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and the Royal Society of Biology and distributed under the Creative Commons Attribution License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Review Articles
Zeng, Jia
Shufean, Md Abu
Molecular-based precision oncology clinical decision making augmented by artificial intelligence
title Molecular-based precision oncology clinical decision making augmented by artificial intelligence
title_full Molecular-based precision oncology clinical decision making augmented by artificial intelligence
title_fullStr Molecular-based precision oncology clinical decision making augmented by artificial intelligence
title_full_unstemmed Molecular-based precision oncology clinical decision making augmented by artificial intelligence
title_short Molecular-based precision oncology clinical decision making augmented by artificial intelligence
title_sort molecular-based precision oncology clinical decision making augmented by artificial intelligence
topic Review Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786281/
https://www.ncbi.nlm.nih.gov/pubmed/34874054
http://dx.doi.org/10.1042/ETLS20210220
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