The DOI matches its Crossref record; 138 of the 138 references it cites were found in a registry or an index.
Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine
Open accessAbstract
Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti‐cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI‐powered cancer care.
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Cites
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Cited by
Artificial Intelligence Applications in Health Care Practice: Scoping Review
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Metadata
- Authors
- Chen, Z., Lin, L., Wu, C., Li, C., Xu, R., & Sun, Y.
- Venue
- Cancer Communications
- Published
- 2021-10-06
- Volume
- 41
- Issue
- 11
- Pages
- 1100-1115
- ISSN
- 1000-467X (ISSN-L)
- 2523-3548
- OpenAlex
- W3201845545
- Cited by
- 285
- References
- 138
- FWCI
- 12.17
- Type
- journal article
- Language
- en
- Open access
- gold
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- Chen, Z., Lin, L., Wu, C., Li, C., Xu, R., & Sun, Y. (2021). Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Communications. https://doi.org/10.1002/cac2.12215
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
