半自动的乳房超声波报告生成使用多式联通大型语言模型和深度学习
Khadija Azhar1, Byoung-Dai Lee2, Shi Sub Byon1
1AI Laboratory, HealthHub Co., Ltd., Seoul, Republic of Korea.
Frontiers in medicine
|February 6, 2026
概括
这项研究引入了一种新的AI框架,用于半自动乳房超声波 (美国) 报告,将深度学习和大型语言模型结合起来. 它显著加快了报告生成速度,并提高了早期乳腺癌诊断的病变检测准确度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 乳腺超声波 (US) 对于早期发现乳腺癌至关重要.
- 生成诊断报告是耗时的,特别是多式联弹性图.
- 现有的工作流程要么是手动的,要么是完全自动化的,集成有限.
研究的目的:
- 开发和评估一个新的框架,用于半自动化乳腺美国报告生成.
- 将放射科医生注释与深度学习和大型语言模型集成在一起,以加强报告.
- 减少放射科医生的工作量,提高乳腺癌诊断的效率.
主要方法:
- 开发了一个结合多式联网大型语言模型和深度学习的框架.
- 该系统使用了来自美国两台机器的2,119张弹性图像和60个注释患者病例.
- 放射科医生注释与图像分类和结构化报告编制相结合.
主要成果:
- 该系统在弹性学分类中实现了高性能 (AUC为0.92,0.91,0.88).
- 报告生成模块在病变检测方面表现出100%的灵敏度.
- 平均报告生成时间为31s (GE医疗保健) 和36s (超级图像).
结论:
- 拟议的框架使准确,高效,和设备可适应的乳房美国报告生成.
- 结合多式联机深度学习和基于提示的大型语言模型推断,显著减少了放射科医生的工作量.
- 该框架显示了在诊断乳腺癌的临床环境中可扩展部署的潜力.
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