On the role of artificial intelligence in medical imaging of COVID-19

Jannis Born; David Beymer; Deepta Rajan; Adam Coy; Vandana V. Mukherjee; Matteo Manica; Prasanth Prasanna; Deddeh Ballah; Michal Guindy; Dorith Shaham; Pallav L. Shah; Emmanouil Karteris; Jan L. Robertus; Maria Gabrani; Michal Rosen-Zvi

doi:10.1016/j.patter.2021.100269

On the role of artificial intelligence in medical imaging of COVID-19

Jannis Born, David Beymer, Deepta Rajan, Adam Coy, Vandana V. Mukherjee, Matteo Manica, Prasanth Prasanna, Deddeh Ballah, Michal Guindy, Dorith Shaham, Pallav L. Shah, Emmanouil Karteris, Jan L. Robertus, Maria Gabrani, Michal Rosen-Zvi

Research output: Contribution to journal › Review article › peer-review

53 Scopus citations

Abstract

Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.

Original language	English
Article number	100269
Journal	Patterns
Volume	2
Issue number	6
DOIs	https://doi.org/10.1016/j.patter.2021.100269
State	Published - 11 Jun 2021

Keywords

artificial intelligence
chest CT
chest ultrasound
chest X-ray
Coronavirus
COVID-19
deep learning
digital healthcare
lung imaging
machine learning
medical imaging
meta-review
PRISMA
SARS-CoV-2

ASJC Scopus subject areas

General Decision Sciences

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.patter.2021.100269

Cite this

@article{3a3f1418050543178808dbfa333c95f8,

title = "On the role of artificial intelligence in medical imaging of COVID-19",

abstract = "Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.",

keywords = "artificial intelligence, chest CT, chest ultrasound, chest X-ray, Coronavirus, COVID-19, deep learning, digital healthcare, lung imaging, machine learning, medical imaging, meta-review, PRISMA, SARS-CoV-2",

author = "Jannis Born and David Beymer and Deepta Rajan and Adam Coy and Mukherjee, {Vandana V.} and Matteo Manica and Prasanth Prasanna and Deddeh Ballah and Michal Guindy and Dorith Shaham and Shah, {Pallav L.} and Emmanouil Karteris and Robertus, {Jan L.} and Maria Gabrani and Michal Rosen-Zvi",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors",

year = "2021",

month = jun,

day = "11",

doi = "10.1016/j.patter.2021.100269",

language = "English",

volume = "2",

journal = "Patterns",

issn = "2666-3899",

publisher = "Cell Press",

number = "6",

}

TY - JOUR

T1 - On the role of artificial intelligence in medical imaging of COVID-19

AU - Born, Jannis

AU - Beymer, David

AU - Rajan, Deepta

AU - Coy, Adam

AU - Mukherjee, Vandana V.

AU - Manica, Matteo

AU - Prasanna, Prasanth

AU - Ballah, Deddeh

AU - Guindy, Michal

AU - Shaham, Dorith

AU - Shah, Pallav L.

AU - Karteris, Emmanouil

AU - Robertus, Jan L.

AU - Gabrani, Maria

AU - Rosen-Zvi, Michal

PY - 2021/6/11

Y1 - 2021/6/11

N2 - Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.

AB - Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.

KW - artificial intelligence

KW - chest CT

KW - chest ultrasound

KW - chest X-ray

KW - Coronavirus

KW - COVID-19

KW - deep learning

KW - digital healthcare

KW - lung imaging

KW - machine learning

KW - medical imaging

KW - meta-review

KW - PRISMA

KW - SARS-CoV-2

UR - http://www.scopus.com/inward/record.url?scp=85107077963&partnerID=8YFLogxK

U2 - 10.1016/j.patter.2021.100269

DO - 10.1016/j.patter.2021.100269

M3 - Review article

AN - SCOPUS:85107077963

SN - 2666-3899

VL - 2

JO - Patterns

JF - Patterns

IS - 6

M1 - 100269

ER -

On the role of artificial intelligence in medical imaging of COVID-19

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this