Artificial intelligence in breast imaging

Potentials and limitations

Ellen B Mendelson*

*Corresponding author for this work

Research output: Contribution to journalReview article

1 Citation (Scopus)

Abstract

OBJECTIVE. The purpose of this article is to discuss potential applications of artificial intelligence (AI) in breast imaging and limitations that may slow or prevent its adoption. CONCLUSION. The algorithms of AI for workflow improvement and outcome analyses are advancing. Using imaging data of high quality and quantity, AI can support breast imagers in diagnosis and patient management, but AI cannot yet be relied on or be responsible for physicians' decisions that may affect survival. Education in AI is urgently needed for physicians.

Original languageEnglish (US)
Pages (from-to)293-299
Number of pages7
JournalAmerican Journal of Roentgenology
Volume212
Issue number2
DOIs
StatePublished - Feb 1 2019

Fingerprint

Artificial Intelligence
Breast
Physicians
Workflow
Education
Survival

Keywords

  • Artificial intelligence in breast imaging
  • Artificial intelligence in radiology
  • Artificial neural networks
  • Computer-aided detection and diagnosis
  • Machine and deep learning

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

@article{a970c39c2e2d4b408690601e01f16900,
title = "Artificial intelligence in breast imaging: Potentials and limitations",
abstract = "OBJECTIVE. The purpose of this article is to discuss potential applications of artificial intelligence (AI) in breast imaging and limitations that may slow or prevent its adoption. CONCLUSION. The algorithms of AI for workflow improvement and outcome analyses are advancing. Using imaging data of high quality and quantity, AI can support breast imagers in diagnosis and patient management, but AI cannot yet be relied on or be responsible for physicians' decisions that may affect survival. Education in AI is urgently needed for physicians.",
keywords = "Artificial intelligence in breast imaging, Artificial intelligence in radiology, Artificial neural networks, Computer-aided detection and diagnosis, Machine and deep learning",
author = "Mendelson, {Ellen B}",
year = "2019",
month = "2",
day = "1",
doi = "10.2214/AJR.18.20532",
language = "English (US)",
volume = "212",
pages = "293--299",
journal = "American Journal of Roentgenology",
issn = "0361-803X",
publisher = "American Roentgen Ray Society",
number = "2",

}

Artificial intelligence in breast imaging : Potentials and limitations. / Mendelson, Ellen B.

In: American Journal of Roentgenology, Vol. 212, No. 2, 01.02.2019, p. 293-299.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Artificial intelligence in breast imaging

T2 - Potentials and limitations

AU - Mendelson, Ellen B

PY - 2019/2/1

Y1 - 2019/2/1

N2 - OBJECTIVE. The purpose of this article is to discuss potential applications of artificial intelligence (AI) in breast imaging and limitations that may slow or prevent its adoption. CONCLUSION. The algorithms of AI for workflow improvement and outcome analyses are advancing. Using imaging data of high quality and quantity, AI can support breast imagers in diagnosis and patient management, but AI cannot yet be relied on or be responsible for physicians' decisions that may affect survival. Education in AI is urgently needed for physicians.

AB - OBJECTIVE. The purpose of this article is to discuss potential applications of artificial intelligence (AI) in breast imaging and limitations that may slow or prevent its adoption. CONCLUSION. The algorithms of AI for workflow improvement and outcome analyses are advancing. Using imaging data of high quality and quantity, AI can support breast imagers in diagnosis and patient management, but AI cannot yet be relied on or be responsible for physicians' decisions that may affect survival. Education in AI is urgently needed for physicians.

KW - Artificial intelligence in breast imaging

KW - Artificial intelligence in radiology

KW - Artificial neural networks

KW - Computer-aided detection and diagnosis

KW - Machine and deep learning

UR - http://www.scopus.com/inward/record.url?scp=85060255520&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060255520&partnerID=8YFLogxK

U2 - 10.2214/AJR.18.20532

DO - 10.2214/AJR.18.20532

M3 - Review article

VL - 212

SP - 293

EP - 299

JO - American Journal of Roentgenology

JF - American Journal of Roentgenology

SN - 0361-803X

IS - 2

ER -