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A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals

Authors :
Sun, Ju
Peng, Le
Li, Taihui
Adila, Dyah
Zaiman, Zach
Melton, Genevieve B.
Ingraham, Nicholas
Murray, Eric
Boley, Daniel
Switzer, Sean
Burns, John L.
Huang, Kun
Allen, Tadashi
Steenburg, Scott D.
Gichoya, Judy Wawira
Kummerfeld, Erich
Tignanelli, Christopher
Publication Year :
2021

Abstract

Importance: An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. Despite significant efforts, many limitations and biases exist in previously developed AI diagnostic models for COVID-19. Utilizing a large set of local and international CXR images, we developed an AI model with high performance on temporal and external validation. Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct, but not replacement, for clinical decision support of COVID-19 diagnosis, which largely hinges on exposure history, signs, and symptoms. While AI-based tools have not yet reached full diagnostic potential in COVID-19, they may still offer valuable information to clinicians taken into consideration along with clinical signs and symptoms.<br />Comment: Check out the medRxiv version at https://doi.org/10.1101/2021.06.04.21258316 for updates

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2106.02118
Document Type :
Working Paper
Full Text :
https://doi.org/10.1101/2021.06.04.21258316