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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Pillai, M; Adapa, K; Das, SK; Mazur, L; Dooley, J; Marks, LB; Thompson, RF; Chera, BS

Published Date

  • September 2019

Published In

Volume / Issue

  • 16 / 9 Pt B

Start / End Page

  • 1267 - 1272

PubMed ID

  • 31492404

Pubmed Central ID

  • 31492404

Electronic International Standard Serial Number (EISSN)

  • 1558-349X

Digital Object Identifier (DOI)

  • 10.1016/j.jacr.2019.06.001

Language

  • eng

Conference Location

  • United States