Artificial intelligence in oncology: Path to implementation. (7th May 2021)

• Record Type: Journal Article
• Authors: Chua, Isaac S.; Gaziel‐Yablowitz, Michal; Korach, Zfania T.; Kehl, Kenneth L.; Levitan, Nathan A.; Arriaga, Yull E.; Jackson, Gretchen P.; Bates, David W.; Hassett, Michael
• Is Part Of: Cancer medicine. Volume 10:Number 12(2021)
• Page Start: 4138
• Page End: 4149
• Publication Date: 2021-05-07
• Subjects: artificial intelligence -- deep learning -- machine learning -- oncology; 616.994005
• DOI: 10.1002/cam4.3935
• Languages: English
• ISSNs: 2045-7634

Abstract

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a lack of standardized research reporting, insufficient clinical validation, workflow and user‐design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data.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. Abstract : Artificial intelligence (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. Overcoming implementation barriers for AI in oncology will require training and educating the oncology workforce in AI; standardizing datasets, research reporting, validation methods, and regulatory standards; and funding and conducting prospective clinical trials that demonstrate improvement in patient outcomes.