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

TL;DR

This study evaluates the performance of a chest X-ray AI diagnostic support tool across 12 U.S. hospitals, highlighting its potential as an adjunct in COVID-19 diagnosis despite current limitations.

Contribution

It presents a large-scale validation of an AI model for COVID-19 detection from chest X-rays across multiple hospitals, addressing previous biases and limitations.

Findings

High performance on temporal validation

Effective as an adjunct, not a replacement

Potential to aid clinical decision-making

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.