Enhancing diagnostic precision in liver lesion analysis using a deep learning-based system: opportunities and challenges

被引：0

作者：

Lee, Jeong Min ^{[1
,2
,3
]}

Bae, Jae Seok ^{[1
]}

机构：

[1] Seoul Natl Univ Hosp, Dept Radiol, Seoul, South Korea

[2] Seoul Natl Univ, Coll Med, Dept Radiol, Seoul, South Korea

[3] Seoul Natl Univ, Inst Radiat Med, Med Res Ctr, Seoul, South Korea

来源：

NATURE REVIEWS CLINICAL ONCOLOGY | 2024年 / 21卷 / 07期

关键词：

FUTURE;

D O I：

10.1038/s41571-024-00887-x

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

A recent study reported the development and validation of the Liver Artificial Intelligence Diagnosis System (LiAIDS), a fully automated system that integrates deep learning for the diagnosis of liver lesions on the basis of contrast-enhanced CT scans and clinical information. This tool improved diagnostic precision, surpassed the accuracy of junior radiologists (and equalled that of senior radiologists) and streamlined patient triage. These advances underscore the potential of artificial intelligence to enhance hepatology care, although challenges to widespread clinical implementation remain.

引用

页码：485 / 486

页数：2

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