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Stargazing through the lens of AI in clinical oncology

Nature Cancer volume 2, pages 1265–1267 (2021)Cite this article

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Cancer multi-omics data has greatly expanded over recent decades, surpassing the human ability to extract meaningful information. The successful implementation of artificial intelligence systems into clinical pipelines to interpret complex datasets, and improve the outcomes of patients with cancer, demands strong validation using real-world evidence while also being mindful of ethical and social aspects.

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Fig. 1: Translating big data into meaningful oncology pipelines with AI.

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Authors and Affiliations

  1. Department of Radiology, Massachusetts General Brigham, Boston, MA, USA

    Constance D. Lehman

  2. Harvard Medical School, Boston, MA, USA

    Constance D. Lehman

  3. Department of Radiology, Department of Biomedical Informatics, Department of Bioengineering, Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA

    Shandong Wu

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  1. Constance D. Lehman
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  2. Shandong Wu
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Correspondence to Constance D. Lehman.

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Competing interests

C.D.L. receives institutional grant/research support from the Breast Cancer Research Foundation, National Cancer Institute, GE Healthcare, Inc. and Hologic, Inc. and is co-founder of Clairity, Inc. S.W. is a scientific consultant and stockholder of COGNISTX, Inc. S.W. receives research grants from the National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Science Foundation, Radiological Society of North America, UPMC Hillman Cancer Center and Amazon.

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Lehman, C.D., Wu, S. Stargazing through the lens of AI in clinical oncology. Nat Cancer 2, 1265–1267 (2021). https://doi.org/10.1038/s43018-021-00307-4

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