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

^*Corresponding author for this work

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

55 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
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2021 The Authors

Keywords

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

UN SDGs

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

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10.1016/j.patter.2021.100269

Cite this

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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.",

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

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

Abstract

Bibliographical note

Keywords

UN SDGs

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