Lim Woo Hyeon, Kim Hyungjin
Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
Tuberc Respir Dis (Seoul). 2025 Apr;88(2):278-291. doi: 10.4046/trd.2024.0062. Epub 2024 Dec 17.
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.
胸部放射学已成为人工智能（AI）得到广泛研究的主要领域。最近的进展凸显了通过人工智能提高放射科医生工作表现的潜力。人工智能有助于检测和分类异常情况，并对正常和异常的解剖结构进行量化。此外，它还通过利用这些量化值来促进预后评估。这篇综述文章将讨论人工智能在胸部放射学方面的最新成果，主要聚焦于深度学习，并探讨这项前沿技术当前的局限性和未来发展方向。
