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 journalReview articlepeer-review

    14 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 languageEnglish
    Article number100269
    JournalPatterns
    Volume2
    Issue number6
    DOIs
    StatePublished - 11 Jun 2021

    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

    ASJC Scopus subject areas

    • Decision Sciences (all)

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