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Open Access Publications from the University of California

Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs.

  • Author(s): Feng, Mary
  • Valdes, Gilmer
  • Dixit, Nayha
  • Solberg, Timothy D
  • et al.
Abstract

Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.

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