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. 2021 Dec;21(12):747-752.
doi: 10.1038/s41568-021-00399-1. Epub 2021 Sep 17.

Artificial intelligence in cancer research, diagnosis and therapy

Olivier Elemento  1 , Christina Leslie  2 , Johan Lundin  3   4   5 , Georgia Tourassi  6
Affiliations

Artificial intelligence in cancer research, diagnosis and therapy

Olivier Elemento et al. Nat Rev Cancer. 2021 Dec.

Abstract

Artificial intelligence and machine learning techniques are breaking into biomedical research and health care, which importantly includes cancer research and oncology, where the potential applications are vast. These include detection and diagnosis of cancer, subtype classification, optimization of cancer treatment and identification of new therapeutic targets in drug discovery. While big data used to train machine learning models may already exist, leveraging this opportunity to realize the full promise of artificial intelligence in both the cancer research space and the clinical space will first require significant obstacles to be surmounted. In this Viewpoint article, we asked four experts for their opinions on how we can begin to implement artificial intelligence while ensuring standards are maintained so as transform cancer diagnosis and the prognosis and treatment of patients with cancer and to drive biological discovery.

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References

    1. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). - DOI
    1. Kooi, T. et al. Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303–312 (2017). - DOI
    1. Pantanowitz, L. et al. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digit. Health 2, e407–e416 (2020). - DOI
    1. Wang, P. et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat. Biomed. Eng. 2, 741–748 (2018). - DOI
    1. Banchereau, R. et al. Molecular determinants of response to PD-L1 blockade across tumor types. Nat. Commun. 12, 3969 (2021). - DOI

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