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RADIOTHERAPY

Prospective clinical deployment of machine learning in radiation oncology

Nature Reviews Clinical Oncology volume 18, pages 605–606 (2021)Cite this article

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Artificial intelligence and machine learning have the potential to make cancer care more accessible, efficient, cost-effective and personalized. However, meticulously planned prospective deployment strategies are required to validate the performance of these technologies in real-world clinical settings and overcome the human trust barrier.

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Acknowledgements

The author gratefully acknowledges research funding from the US National Institutes of Health (grants R37-CA222215, R01-CA233487 and R41-CA243722, and contract 75N92020D00018).

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  1. Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA

    Issam El Naqa

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  1. Issam El Naqa
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Correspondence to Issam El Naqa.

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The author has acted as an advisor of Endectra LLC.

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El Naqa, I. Prospective clinical deployment of machine learning in radiation oncology. Nat Rev Clin Oncol 18, 605–606 (2021). https://doi.org/10.1038/s41571-021-00541-w

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