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 Thompson, Bhishamjit S. Chera

Research output: Contribution to journalArticle

2 Citations (Scopus)

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)
JournalJournal of the American College of Radiology
DOIs
StatePublished - Jan 1 2019

Fingerprint

Radiation Oncology
Artificial Intelligence
Radiotherapy
Safety
Clinical Decision Support Systems
Workflow
Machine Learning
Therapeutics

Keywords

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

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy. / Pillai, Malvika; Adapa, Karthik; Das, Shiva K.; Mazur, Lukasz; Dooley, John; Marks, Lawrence B.; Thompson, Reid; Chera, Bhishamjit S.

In: Journal of the American College of Radiology, 01.01.2019.

Research output: Contribution to journalArticle

Pillai, Malvika ; Adapa, Karthik ; Das, Shiva K. ; Mazur, Lukasz ; Dooley, John ; Marks, Lawrence B. ; Thompson, Reid ; Chera, Bhishamjit S. / Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy. In: Journal of the American College of Radiology. 2019.
@article{399b77d5da2749f1a988388d2d10eba6,
title = "Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy",
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 = "artificial intelligence, machine learning, quality and safety, Radiation oncology, radiation therapy",
author = "Malvika Pillai and Karthik Adapa and Das, {Shiva K.} and Lukasz Mazur and John Dooley and Marks, {Lawrence B.} and Reid Thompson and Chera, {Bhishamjit S.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1016/j.jacr.2019.06.001",
language = "English (US)",
journal = "Journal of the American College of Radiology",
issn = "1558-349X",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy

AU - Pillai, Malvika

AU - Adapa, Karthik

AU - Das, Shiva K.

AU - Mazur, Lukasz

AU - Dooley, John

AU - Marks, Lawrence B.

AU - Thompson, Reid

AU - Chera, Bhishamjit S.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - artificial intelligence

KW - machine learning

KW - quality and safety

KW - Radiation oncology

KW - radiation therapy

UR - http://www.scopus.com/inward/record.url?scp=85069506397&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069506397&partnerID=8YFLogxK

U2 - 10.1016/j.jacr.2019.06.001

DO - 10.1016/j.jacr.2019.06.001

M3 - Article

JO - Journal of the American College of Radiology

JF - Journal of the American College of Radiology

SN - 1558-349X

ER -