Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review

Lim, Woo Hyeon; Kim, Hyungjin

doi:.0..

Tuberc Respir Dis > Epub ahead of print

Review

DOI: https://doi.org/10.4046/trd.2024.0062 [Epub ahead of print]
Published online December 17, 2024.

Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review

Woo Hyeon Lim, M.D.¹

, Hyungjin Kim, M.D., Ph.D.^1,2

¹Department of Radiology, Seoul National University Hospital, Seoul, Korea
²Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea

Correspondence: Hyungjin Kim, Tel: 82-2-2072-2254, Fax: 82-2-743-6385,
Email: khj.snuh@gmail.com

Received: 2 May 2024 • Revised: 2 September 2024 • Accepted: 11 December 2024

Abstract

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Key Words: Artificial Intelligence, Deep Learning, Thoracic Radiology