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

Malvika Pillai, Karthik Adapa, Shiva K. Das, Lukasz Mazur, John Dooley, Lawrence B. Marks, Reid F. Thompson, Bhishamjit S. Chera

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1267-1272
Number of pages6
JournalJournal of the American College of Radiology
Volume16
Issue number9
DOIs
StatePublished - Sep 2019

Keywords

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

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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