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Artificial intelligence in cancer research, diagnosis and therapy

Nature Reviews Cancer volume 21, pages 747–752 (2021)Cite this article

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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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Acknowledgements

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.

Author information

Authors and Affiliations

  1. Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, Cornell University, New York, NY, USA

    Olivier Elemento

  2. Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Christina Leslie

  3. Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden

    Johan Lundin

  4. Institute for Molecular Medicine Finland — FIMM, University of Helsinki, Helsinki, Finland

    Johan Lundin

  5. iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland

    Johan Lundin

  6. National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Georgia Tourassi

Authors
  1. Olivier Elemento
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  4. Georgia Tourassi
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Contributions

Olivier Elemento

Olivier Elemento is a professor of physiology and biophysics at Weill Cornell Medicine (WCM) and Cornell University. Since 2017, he has been Director of the Caryl and Israel Englander Institute for Precision Medicine, a multidisciplinary institute that draws on more than 100 faculty members from nearly all basic and clinical departments at Cornell University. Its mission is to use genomics, artificial intelligence (AI) and other technologies to develop and bring highly personalized medicine to patients at WCM’s affiliated hospital, NewYork-Presbyterian Hospital (NYPH), and elsewhere. The institute also fosters patient-centred basic and clinical research in the areas of genomics, systems biology, AI and data science. Olivier Elemento is funded by numerous NIH grants, foundation grants, NIH contracts and industry alliances. He has published more than 320 articles in the areas of precision medicine, genomics, computational biology, AI, systems biology and drug discovery. He has led the development of novel clinical genomics assays, including whole-exome sequencing offered to patients at WCM and NYPH, and is currently leading a large multidisease effort to bring whole-genome sequencing into clinical practice at WCM and NYPH. He co-founded two venture capital-funded companies: Volastra Therapeutics (with Lew Cantley and Sam Bakhoum) and OneThree Biotech (with Neel Madhukar). He serves on the scientific advisory boards of Volastra, OneThree Biotech, Owkin, Freenome and several other companies.

Christina Leslie

Christina Leslie is a member of the Computational and Systems Biology Program of the Sloan Kettering Institute at Memorial Sloan Kettering Cancer Center and a Professor of Physiology, Biophysics and Systems Biology in the Graduate School of Medical Sciences at WCM. She has pioneered machine learning methods for understanding the genomics of gene regulation, with applications to basic and cancer immunology, cancer biology and development.

Johan Lundin

Johan Lundin is a professor of medical technology at Karolinska Institutet, Stockholm, Sweden, and a research director and group leader at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland. His overall research aims are to study the use of digital technologies and AI for improvement of diagnostics and care of the individual patient. In addition to research, he has together with his co-workers developed technologies for diagnostic decision support; for example, cloud-based and mobile solutions that allow the diagnostic process to be performed using AI-supported analysis in both high-resource and low-resource settings. He is also co-founder of Aiforia Technologies, a spin-off company of FIMM developing medical image-based AI.

Georgia Tourassi

Georgia Tourassi is Director of the National Center for Computational Sciences and the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory. Concurrently, she holds appointments as an adjunct professor of radiology at Duke University and the University of Tennessee at Knoxville. Her research interests include high-performance computing and AI in biomedicine.

Corresponding authors

Correspondence to Olivier Elemento, Christina Leslie, Johan Lundin or Georgia Tourassi.

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Competing interests

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 21, 747–752 (2021). https://doi.org/10.1038/s41568-021-00399-1

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