Application of artificial intelligence technology in oncology: towards the establishment of precision medicine
Open accessAbstract
In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.
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Very few machine learning multi-omics publications have been translated into clinical practice.
“Still, only very few have been translated into clinical practice, for example, Idx (diabetic retinopathy detection), FerriSmart (measure liver iron concentration) and SubtleMR (image processing software for radiology) (Benjamens et al., 2020;Hamamoto et al., 2020).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Hamamoto et al., 2020
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References 187 · Cited by 207
Research works printed but not linked: 9
- [2]Moor J. The Dartmouth College Artificial Intelligence Conference: The Next Fifty years AI Mag. 2006 27 87 89
- [21]Russell S. Norvig P. Artificial Intelligence: A Modern Approach Prentice Hall Upper Saddle River, NJ, USA 1995
- [23]Nillson M. Artificial Intelligence: A New Synthesis Morgan Kaufmann Burlington, MA, USA 1998
- [24]Lindsay R. Buchanan B. Feigenbaum E. Lederberg J. Applications of Artificial Intelligence for Organic Chemistry: The DENDRAL Project McGraw-Hill New York, NY, USA 1980
- [25]Shortliffe E.H. Computer-Based Medical Consultations: MYCIN Elsevier North Holland, NY, USA 1976
- [37]Yasutomi S. Arakaki T. Hamamoto R. Shadow Detection for Ultrasound Images Using Unlabeled Data and Synthetic Shadows arXiv 2019 1908.01439
- [47]Huang F. Zhang Z. Wang N. Li N. Du M. Hao T. Zhan J. A new direction to promote the implementation of artificial intelligence in natural clinical settings arXiv 2019 1905.02940
- [108]Romero A. Carrier P.L. Erraqabi A. Sylvain T. Auvolat A. Dejoie E. Legault M.A. Dubé M.P. Hussin J.G. Bengio Y. Diet Networks: Thin Parameters for Fat Genomics arXiv 2016 1611.09340
- [163]Samek W. Montavon G. Lapuschkin S. Anders C.J. Müller K.R. Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond arXiv 2020 2003.07631
- Authors
- Hamamoto, R., Suvarna, K., Yamada, M., Kobayashi, K., Shinkai, N., Miyake, M., Takahashi, M., Jinnai, S., Shimoyama, R., Sakai, A., Takasawa, K., Bolatkan, A., Shozu, K., Dozen, A., Machino, H., Takahashi, S., Asada, K., Komatsu, M., Sese, J., & Kaneko, S.
- Affiliations
- Tokyo Medical and Dental University (JP)
- Indian Institute of Technology Bombay (IN)
- National Cancer Center Hospital (JP)
- National Cancer Centre Japan (JP)
- National Cancer Center Research Institute (JP)
- RIKEN Center for Advanced Intelligence Project (JP)
- Corresponding author
- Hamamoto, Ryuji
- ORCID
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2020-11-26
- Volume
- 12
- Issue
- 12
- Pages
- 3532
- ISSN
- 2072-6694 (ISSN-L)
- PMC
- PMC7760590
- OpenAlex
- W3110223342
- Cited by
- 207
- References
- 187
- Citations by year
- 2026: 14 · 2025: 43 · 2024: 44 · 2023: 39 · 2022: 36 · 2021: 29 · 2020: 2
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancers (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Japan Science and Technology Agency (JPMJCR1689, PMJCR18Y4)
- Japan Society for the Promotion of Science (JP18H04908, 20K17982)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in healthcare, oncology, precision medicine, AI medical devices, radiology, medical data analysis, history of artificial intelligence, clinical implementation of AI
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