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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C.L. acknowledges A. Kundaje, W. S. Noble, Q. Morris and T. Norman for helpful comments on the text. J.L. warmly thanks H. B. Burke and N. Linder for constructive comments on and valuable input to the text. J.L. also thanks research collaborators and group members at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland, the Department of Global Public Health, Karolinska Institutet, Sweden, the Kinondo Kwetu Health Center, Kenya, the Muhimbili University of Health and Allied Sciences, Tanzania, and Aiforia Technologies Oy, Helsinki, who all contributed to the cited studies on applied artificial intelligence. J.L. acknowledges funding from the Erling-Persson Family Foundation, the Swedish Research Council, the Sigrid Jusélius Foundation, Finska Läkaresällskapet, Medicinska Understödsföreningen Liv och Hälsa and the iCAN Digital Precision Cancer Medicine Flagship project. This article was authored in part by UT-Battelle LLC under contract no. DE-AC05-00OR22725 with the US Department of Energy. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this article, or allow others to do so, for US Government purposes.
O.E. is supported by Janssen, Johnson & Johnson, AstraZeneca, Volastra and Eli Lilly research grants. He is a scientific advisor to and equity holder in Freenome, Owkin, Volastra Therapeutics and OneThree Biotech and is a paid scientific advisor to Champions Oncology. J.L. is a co-founder, shareholder and member of the Board of Directors of and receives consultation fees from Aiforia Technologies Oy. J.L. is also a founding member and an unpaid member of the Board of Advisors of the European Society of Digital and Integrative Pathology. C.L. and G.T. declare no competing interests.
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Elemento, O., Leslie, C., Lundin, J. et al. Artificial intelligence in cancer research, diagnosis and therapy. Nat Rev Cancer (2021). https://doi.org/10.1038/s41568-021-00399-1