Calum Cunningham

AI
How AI integrated workflow can free radiologists to focus on patients
An integrated workflow network enabled by artificial intelligence (AI) can help radiologists easily access critical patient data to inform a more accurate report that facilitates better care. Calum Cunningham of Nuance Communications explains how.
August 24, 2022
2020 03 04 21 21 6197 Artificial Intelligence Ai Hand 400 20200304215813
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Figure: Improved image quality with a generative adversarial network architecture for low- to high-field-strength image translation, called LowGAN. (A–C) Top: High-field-strength (3-T) and low-field-strength (64-mT) images and LowGAN outputs for a single participant across (A) fluid-attenuated inversion recovery (FLAIR), (B) T1-weighted (T1w), and (C) T2-weighted (T2w) contrasts. Bottom: Graphs demonstrate the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and feature similarity index (FSIM) between high field strength and low field strength (64-mT) and high field strength and LowGAN outputs. a.u. = arbitrary units.****p ≤ 0.0001. Images and caption courtesy of Radiology and the RSNA.
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