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Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information
Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year’s State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers spe...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620610/ https://www.ncbi.nlm.nih.gov/pubmed/36316731 http://dx.doi.org/10.1186/s40164-022-00333-7 |
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author | Hamamoto, Ryuji Koyama, Takafumi Kouno, Nobuji Yasuda, Tomohiro Yui, Shuntaro Sudo, Kazuki Hirata, Makoto Sunami, Kuniko Kubo, Takashi Takasawa, Ken Takahashi, Satoshi Machino, Hidenori Kobayashi, Kazuma Asada, Ken Komatsu, Masaaki Kaneko, Syuzo Yatabe, Yasushi Yamamoto, Noboru |
author_facet | Hamamoto, Ryuji Koyama, Takafumi Kouno, Nobuji Yasuda, Tomohiro Yui, Shuntaro Sudo, Kazuki Hirata, Makoto Sunami, Kuniko Kubo, Takashi Takasawa, Ken Takahashi, Satoshi Machino, Hidenori Kobayashi, Kazuma Asada, Ken Komatsu, Masaaki Kaneko, Syuzo Yatabe, Yasushi Yamamoto, Noboru |
author_sort | Hamamoto, Ryuji |
collection | PubMed |
description | Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year’s State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation. |
format | Online Article Text |
id | pubmed-9620610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-96206102022-11-01 Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information Hamamoto, Ryuji Koyama, Takafumi Kouno, Nobuji Yasuda, Tomohiro Yui, Shuntaro Sudo, Kazuki Hirata, Makoto Sunami, Kuniko Kubo, Takashi Takasawa, Ken Takahashi, Satoshi Machino, Hidenori Kobayashi, Kazuma Asada, Ken Komatsu, Masaaki Kaneko, Syuzo Yatabe, Yasushi Yamamoto, Noboru Exp Hematol Oncol Review Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year’s State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation. BioMed Central 2022-10-31 /pmc/articles/PMC9620610/ /pubmed/36316731 http://dx.doi.org/10.1186/s40164-022-00333-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Hamamoto, Ryuji Koyama, Takafumi Kouno, Nobuji Yasuda, Tomohiro Yui, Shuntaro Sudo, Kazuki Hirata, Makoto Sunami, Kuniko Kubo, Takashi Takasawa, Ken Takahashi, Satoshi Machino, Hidenori Kobayashi, Kazuma Asada, Ken Komatsu, Masaaki Kaneko, Syuzo Yatabe, Yasushi Yamamoto, Noboru Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information |
title | Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information |
title_full | Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information |
title_fullStr | Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information |
title_full_unstemmed | Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information |
title_short | Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information |
title_sort | introducing ai to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620610/ https://www.ncbi.nlm.nih.gov/pubmed/36316731 http://dx.doi.org/10.1186/s40164-022-00333-7 |
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