Artificial intelligence in oncology: Path to implementation

Autor: David W. Bates, Michael J. Hassett, Isaac S. Chua, Kenneth L. Kehl, Gretchen Purcell Jackson, Zfania Tom Korach, Nathan A Levitan, Michal Gaziel-Yablowitz, Yull E. Arriaga
Rok vydání: 2021
Předmět:
Research Report
0301 basic medicine
Oncology
Cancer Research
medicine.medical_specialty
Computer science
Data management
Review
Bioinfomatics
Medical Oncology
GeneralLiterature_MISCELLANEOUS
Field (computer science)
03 medical and health sciences
0302 clinical medicine
Bias
Internal medicine
Image Interpretation
Computer-Assisted
medicine
Humans
Radiology
Nuclear Medicine and imaging
Precision Medicine
RC254-282
business.industry
Data Collection
deep learning
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Predictive analytics
Decision Support Systems
Clinical
artificial intelligence
Variety (cybernetics)
Technical performance
machine learning
ComputingMethodologies_PATTERNRECOGNITION
030104 developmental biology
Workflow
030220 oncology & carcinogenesis
Workforce
Artificial intelligence
business
PATH (variable)
Zdroj: Cancer Medicine, Vol 10, Iss 12, Pp 4138-4149 (2021)
Cancer Medicine
ISSN: 2045-7634
DOI: 10.1002/cam4.3935
Popis: 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a lack of standardized research reporting, insufficient clinical validation, workflow and user‐design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data.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.
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.
Databáze: OpenAIRE
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