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  • Perspective
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Artificial intelligence in radiation oncology

Nature Reviews Clinical Oncology volume 17, pages 771–781 (2020)Cite this article

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Abstract

Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. AI platforms excel in recognizing complex patterns in medical data and provide a quantitative, rather than purely qualitative, assessment of clinical conditions. Accordingly, AI could have particularly transformative applications in radiation oncology given the multifaceted and highly technical nature of this field of medicine with a heavy reliance on digital data processing and computer software. Indeed, AI has the potential to improve the accuracy, precision, efficiency and overall quality of radiation therapy for patients with cancer. In this Perspective, we first provide a general description of AI methods, followed by a high-level overview of the radiation therapy workflow with discussion of the implications that AI is likely to have on each step of this process. Finally, we describe the challenges associated with the clinical development and implementation of AI platforms in radiation oncology and provide our perspective on how these platforms might change the roles of radiotherapy medical professionals.

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Fig. 1: Applications of AI in the radiation therapy workflow.
Fig. 2: Staff involvement and patient-facing steps in the radiation therapy workflow.
Fig. 3: Potential implications of applying AI in radiation oncology for members of the radiation therapy workforce.

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Acknowledgements

The authors acknowledge financial support from the US NIH (grants U24CA194354, U01CA190234, U01CA209414 and R35CA220523).

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Authors and Affiliations

  1. Artificial Intelligence in Medicine (AIM) Program, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Elizabeth Huynh, Ahmed Hosny, Danielle S. Bitterman, Daphne A. Haas-Kogan, Benjamin Kann, Hugo J. W. L. Aerts & Raymond H. Mak

  2. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Elizabeth Huynh, Ahmed Hosny, Christian Guthier, Danielle S. Bitterman, Daphne A. Haas-Kogan, Benjamin Kann, Hugo J. W. L. Aerts & Raymond H. Mak

  3. Computational Health Informatics Program, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Danielle S. Bitterman

  4. Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands

    Steven F. Petit

  5. Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Hugo J. W. L. Aerts

  6. Department of Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands

    Hugo J. W. L. Aerts

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  1. Elizabeth Huynh
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Contributions

E.H., A.H. and R.H.M. researched data for the article; E.H., A.H., H.J.W.L.A. and R.H.M. contributed substantially to the discussion of content; and E.H., A.H., C.G., D.S.B., H.J.W.L.A. and R.H.M. wrote the article. All authors reviewed/edited the manuscript before submission.

Corresponding author

Correspondence to Hugo J. W. L. Aerts.

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Competing Interests

A.H. is a shareholder of and receives consulting fees from Altis Labs. The other authors declare no competing interests.

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Nature Reviews Clinical Oncology thanks I. El Naqa, G. Valdes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Huynh, E., Hosny, A., Guthier, C. et al. Artificial intelligence in radiation oncology. Nat Rev Clin Oncol 17, 771–781 (2020). https://doi.org/10.1038/s41571-020-0417-8

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