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J Am Coll Radiol. 2019 Sep;16(9 Pt B):1267-1272. doi: 10.1016/j.jacr.2019.06.001.

Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy.

Author information

1
Carolina Health Informatics Program, University of North Carolina, Chapel Hill, North Carolina.
2
Department of Radiation Oncology, University of North Carolina, Chapel Hill, North Carolina.
3
Carolina Health Informatics Program, University of North Carolina, Chapel Hill, North Carolina; Department of Radiation Oncology, University of North Carolina, Chapel Hill, North Carolina.
4
Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon; Division of Hospital and Specialty Medicine, VA Portland Healthcare System, Portland, Oregon. Electronic address: thompsre@ohsu.edu.

Abstract

Within artificial intelligence, machine learning (ML) efforts in radiation oncology have augmented the transition from generalized to personalized treatment delivery. Although their impact on quality and safety of radiation therapy has been limited, they are increasingly being used throughout radiation therapy workflows. Various data-driven approaches have been used for outcome prediction, CT simulation, clinical decision support, knowledge-based planning, adaptive radiation therapy, plan validation, machine quality assurance, and process quality assurance; however, there are many challenges that need to be addressed with the creation and usage of ML algorithms as well as the interpretation and dissemination of findings. In this review, the authors present current applications of ML in radiation oncology quality and safety initiatives, discuss challenges faced by the radiation oncology community, and suggest future directions.

KEYWORDS:

Radiation oncology; artificial intelligence; machine learning; quality and safety; radiation therapy

PMID:
31492404
DOI:
10.1016/j.jacr.2019.06.001

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