Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Reid Thompson, Gilmer Valdes, Clifton D. Fuller, Colin M. Carpenter, Olivier Morin, Sanjay Aneja, William D. Lindsay, Hugo J.W.L. Aerts, Barbara Agrimson, Curtiland Deville, Seth A. Rosenthal, James B. Yu, Charles Thomas

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the “fourth” industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.

Original languageEnglish (US)
JournalRadiotherapy and Oncology
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Radiation Oncology
Artificial Intelligence
Radiology
Industry
Medicine
Pathology
Technology
Delivery of Health Care

Keywords

  • Artificial intelligence
  • Deep learning
  • Machine learning

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Radiology Nuclear Medicine and imaging

Cite this

Artificial intelligence in radiation oncology : A specialty-wide disruptive transformation? / Thompson, Reid; Valdes, Gilmer; Fuller, Clifton D.; Carpenter, Colin M.; Morin, Olivier; Aneja, Sanjay; Lindsay, William D.; Aerts, Hugo J.W.L.; Agrimson, Barbara; Deville, Curtiland; Rosenthal, Seth A.; Yu, James B.; Thomas, Charles.

In: Radiotherapy and Oncology, 01.01.2018.

Research output: Contribution to journalArticle

Thompson, R, Valdes, G, Fuller, CD, Carpenter, CM, Morin, O, Aneja, S, Lindsay, WD, Aerts, HJWL, Agrimson, B, Deville, C, Rosenthal, SA, Yu, JB & Thomas, C 2018, 'Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?', Radiotherapy and Oncology. https://doi.org/10.1016/j.radonc.2018.05.030
Thompson, Reid ; Valdes, Gilmer ; Fuller, Clifton D. ; Carpenter, Colin M. ; Morin, Olivier ; Aneja, Sanjay ; Lindsay, William D. ; Aerts, Hugo J.W.L. ; Agrimson, Barbara ; Deville, Curtiland ; Rosenthal, Seth A. ; Yu, James B. ; Thomas, Charles. / Artificial intelligence in radiation oncology : A specialty-wide disruptive transformation?. In: Radiotherapy and Oncology. 2018.
@article{159389704cf449778756e649ac6db61b,
title = "Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?",
abstract = "Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the “fourth” industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.",
keywords = "Artificial intelligence, Deep learning, Machine learning",
author = "Reid Thompson and Gilmer Valdes and Fuller, {Clifton D.} and Carpenter, {Colin M.} and Olivier Morin and Sanjay Aneja and Lindsay, {William D.} and Aerts, {Hugo J.W.L.} and Barbara Agrimson and Curtiland Deville and Rosenthal, {Seth A.} and Yu, {James B.} and Charles Thomas",
year = "2018",
month = "1",
day = "1",
doi = "10.1016/j.radonc.2018.05.030",
language = "English (US)",
journal = "Radiotherapy and Oncology",
issn = "0167-8140",
publisher = "Elsevier Ireland Ltd",

}

TY - JOUR

T1 - Artificial intelligence in radiation oncology

T2 - A specialty-wide disruptive transformation?

AU - Thompson, Reid

AU - Valdes, Gilmer

AU - Fuller, Clifton D.

AU - Carpenter, Colin M.

AU - Morin, Olivier

AU - Aneja, Sanjay

AU - Lindsay, William D.

AU - Aerts, Hugo J.W.L.

AU - Agrimson, Barbara

AU - Deville, Curtiland

AU - Rosenthal, Seth A.

AU - Yu, James B.

AU - Thomas, Charles

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the “fourth” industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.

AB - Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the “fourth” industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.

KW - Artificial intelligence

KW - Deep learning

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85048335962&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048335962&partnerID=8YFLogxK

U2 - 10.1016/j.radonc.2018.05.030

DO - 10.1016/j.radonc.2018.05.030

M3 - Article

C2 - 29907338

AN - SCOPUS:85048335962

JO - Radiotherapy and Oncology

JF - Radiotherapy and Oncology

SN - 0167-8140

ER -