Artificial intelligence in oncology: Path to implementation
Abstract In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer‐related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting cli...
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doaj-92dc767e17844921b6e4308a8d2e12d62021-06-17T09:30:45ZengWileyCancer Medicine2045-76342021-06-0110124138414910.1002/cam4.3935Artificial intelligence in oncology: Path to implementationIsaac S. Chua0Michal Gaziel‐Yablowitz1Zfania T. Korach2Kenneth L. Kehl3Nathan A. Levitan4Yull E. Arriaga5Gretchen P. Jackson6David W. Bates7Michael Hassett8Division of General Internal Medicine and Primary Care Department of Medicine Brigham and Women's Hospital Boston MA USADivision of General Internal Medicine and Primary Care Department of Medicine Brigham and Women's Hospital Boston MA USADivision of General Internal Medicine and Primary Care Department of Medicine Brigham and Women's Hospital Boston MA USAHarvard Medical School Boston MA USAIBM Watson Health Cambridge MA USAIBM Watson Health Cambridge MA USAIBM Watson Health Cambridge MA USADivision of General Internal Medicine and Primary Care Department of Medicine Brigham and Women's Hospital Boston MA USAHarvard Medical School Boston MA USAAbstract In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer‐related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high‐value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user‐design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration.https://doi.org/10.1002/cam4.3935artificial intelligencedeep learningmachine learningoncology |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Isaac S. Chua Michal Gaziel‐Yablowitz Zfania T. Korach Kenneth L. Kehl Nathan A. Levitan Yull E. Arriaga Gretchen P. Jackson David W. Bates Michael Hassett |
spellingShingle |
Isaac S. Chua Michal Gaziel‐Yablowitz Zfania T. Korach Kenneth L. Kehl Nathan A. Levitan Yull E. Arriaga Gretchen P. Jackson David W. Bates Michael Hassett Artificial intelligence in oncology: Path to implementation Cancer Medicine artificial intelligence deep learning machine learning oncology |
author_facet |
Isaac S. Chua Michal Gaziel‐Yablowitz Zfania T. Korach Kenneth L. Kehl Nathan A. Levitan Yull E. Arriaga Gretchen P. Jackson David W. Bates Michael Hassett |
author_sort |
Isaac S. Chua |
title |
Artificial intelligence in oncology: Path to implementation |
title_short |
Artificial intelligence in oncology: Path to implementation |
title_full |
Artificial intelligence in oncology: Path to implementation |
title_fullStr |
Artificial intelligence in oncology: Path to implementation |
title_full_unstemmed |
Artificial intelligence in oncology: Path to implementation |
title_sort |
artificial intelligence in oncology: path to implementation |
publisher |
Wiley |
series |
Cancer Medicine |
issn |
2045-7634 |
publishDate |
2021-06-01 |
description |
Abstract In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer‐related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high‐value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user‐design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration. |
topic |
artificial intelligence deep learning machine learning oncology |
url |
https://doi.org/10.1002/cam4.3935 |
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