Improving mammography interpretation for both novice and experienced readers: a comparative study of two commercial artificial intelligence software.

Hee Jeong KimWoo Jung ChoiHye Yun GwonSeo Jin JangEun Young ChaeHee Jung ShinJoo Hee ChaHak Hee Kim

Published in: European radiology (2023)

• Mammography interpretation remains challenging and is subject to a wide range of interobserver variability. • In this multi-reader study, two commercial AI software improved the sensitivity of mammography interpretation by both novice and experienced readers. The type of AI software used did not significantly impact performance changes. • Commercial AI software may effectively support mammography interpretation irrespective of the experience level of human readers.

Keyphrases

artificial intelligence
contrast enhanced
machine learning
big data
deep learning
image quality
data analysis
magnetic resonance imaging
endothelial cells
computed tomography
magnetic resonance