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

IF 81.1 1区医学 Q1 ONCOLOGY

Nature Reviews Clinical Oncology Pub Date : 2024-03-22 DOI:10.1038/s41571-024-00887-x

Jeong Min Lee, Jae Seok Bae

引用次数: 0

Abstract

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.

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利用基于深度学习的系统提高肝脏病变分析的诊断精度：机遇与挑战。

最近的一项研究报告了肝脏人工智能诊断系统（LiAIDS）的开发和验证，这是一个全自动系统，集成了深度学习功能，可根据对比增强 CT 扫描和临床信息诊断肝脏病变。该工具提高了诊断精确度，超过了初级放射科医生的准确度（与高级放射科医生相当），并简化了病人分流。这些进展凸显了人工智能在加强肝病治疗方面的潜力，尽管广泛临床应用仍面临挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Reviews Clinical Oncology 医学-肿瘤学

CiteScore

99.40

自引率

0.40%

发文量

114

审稿时长

6-12 weeks

期刊介绍： Nature Reviews publishes clinical content authored by internationally renowned clinical academics and researchers, catering to readers in the medical sciences at postgraduate levels and beyond. Although targeted at practicing doctors, researchers, and academics within specific specialties, the aim is to ensure accessibility for readers across various medical disciplines. The journal features in-depth Reviews offering authoritative and current information, contextualizing topics within the history and development of a field. Perspectives, News & Views articles, and the Research Highlights section provide topical discussions, opinions, and filtered primary research from diverse medical journals.