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Stargazing through the lens of AI in clinical oncology

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.

References

  1. Berry, S. et al. Science 372, eaba2609 (2021).

    Article  CAS  Google Scholar 

  2. Aboutalib, S. S. et al. Clin. Cancer Res. 24, 5902–5909 (2018).

    Article  Google Scholar 

  3. McKinney, S. M. et al. Nature 577, 89–94 (2020).

    Article  CAS  Google Scholar 

  4. Hiremath, A. et al. Lancet Digit. Health 3, e445–e454 (2021).

    Article  Google Scholar 

  5. Yala, A. et al. Sci. Transl. Med. 13, eaba4373 (2021).

    Article  Google Scholar 

  6. McIntosh, C. et al. Nat. Med. 27, 999–1005 (2021).

    Article  CAS  Google Scholar 

  7. Lehman, C. D. et al. JAMA Intern. Med. 175, 1828–1837 (2015).

    Article  Google Scholar 

  8. Lotter, W. et al. Nat. Med. 27, 244–249 (2021).

    Article  CAS  Google Scholar 

  9. Freeman, K. et al. BMJ 374, n1872 (2021).

    Article  Google Scholar 

  10. Buolamwini, J. & Gebru, T. Proc. Mach. Learn. Res. 81, 77–91 (2018).

    Google Scholar 

  11. Bridge to Artificial Intelligence (Bridge2AI) (NIH, 2021); https://commonfund.nih.gov/bridge2ai

  12. Kaushal, A., Altman, R. & Langlotz, C. JAMA 324, 1212–1213 (2020).

    Article  Google Scholar 

  13. Rieke, N. et al. NPJ Digit. Med. 3, 119 (2020).

    Article  Google Scholar 

  14. Steiner, D. F. et al. JAMA Netw. Open 3, e2023267 (2020).

    Article  Google Scholar 

Download references

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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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