医学成像的视觉语言基础模型:对当前实践和创新的审查
Ji Seung Ryu1, Hyunyoung Kang2, Yuseong Chu1
1Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.
Biomedical engineering letters
|September 8, 2025
概括
基金会模型,特别是视觉语言模型 (VLMs),正在改变医疗保健的AI. 本综述考察了2022-2024年的医疗VLM,详细介绍了它们的应用,数据集和未来潜力.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 基础模型,包括大型语言模型和视觉语言模型 (VLMs),已经彻底改变了AI.
- 这些模型通过整合计算机视觉和自然语言处理,在各种应用程序中实现高效,可扩展和多模式学习.
- 在医学上,他们将成像数据与报告和笔记中的文本结合起来,以改善诊断.
研究的目的:
- 从2022年到2024年,系统地审查医疗VLM的最新进展.
- 专注于模式特定的方法和医疗成像中的定制应用.
- 提供结构化的分类学,分析数据集,并审查实际应用.
主要方法:
- 在2022年至2024年之间发表的医学VLM的系统文献综述.
- 使用结构化分类学对模型的分类.
- 数据集,应用程序,挑战和未来方向的分析.
主要成果:
- 确定医疗VLM中的关键进展.
- 开发一种分类学来分类现有模型.
- 审查基本数据集和医学成像中的实际应用.
结论:
- 医疗VLM在简化诊断和提高准确性方面显示出显著的前景.
- 需要进一步的研究来应对挑战,并改善医疗保健的可访问性.
- 未来的方向侧重于加强基础模型在医疗应用中的影响.
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