Evaluating diagnostic content of AI-generated chest radiography: A multi-center visual Turing test.

ABSTRACT:

Background

Accurate interpretation of chest radiographs requires years of medical training, and many countries face a shortage of medical professionals to meet such requirements. Recent advancements in artificial intelligence (AI) have aided diagnoses; however, their performance is often limited due to data imbalance. The aim of this study was to augment imbalanced medical data using generative adversarial networks (GANs) and evaluate the clinical quality of the generated images via a multi-center visual Turing test.

Methods

Using six chest radiograph datasets, (MIMIC, CheXPert, CXR8, JSRT, VBD, and OpenI), starGAN v2 generated chest radiographs with specific pathologies. Five board-certified radiologists from three university hospitals, each with at least five years of clin

SUBMITTER: Myong Y 

PROVIDER: S-EPMC10096231 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature