医学成像中的视觉语言基础模型:对诊断和分析应用的系统审查和元分析
Yiyao Sun1, Xinran Wen2, Yan Zhang3
1School of Intelligent Medicine, China Medical University, Shenyang, Liaoning 110122, PR China.
Computer methods and programs in biomedicine
|May 27, 2025
概括
视觉语言基础模型 (VLMs) 在人工智能辅助的医学诊断和治疗方面显示出强大的潜力. 本次审查强调了它们在分类和细分等任务中的表现,强调了它们在医疗保健中的价值.
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
背景情况:
- 视觉语言基础模型 (VLMs) 是新兴的人工智能工具,在医疗保健领域具有重大潜力.
- 它们的应用涵盖了人工智能辅助的诊断和治疗,推动医疗任务的创新.
研究的目的:
- 系统地审查和元分析VLM在医学成像中的价值和前景.
- 突出VLM在医疗保健应用中的突破性机会.
主要方法:
- 从主要数据库 (PubMed,Embase,Web of Science,IEEE) 的研究进行系统审查和元分析,截至2024年12月31日.
- 包括最先进的VLM开发和应用在医学成像中的应用.
- 用于分类,细分,报告生成和视觉问题答案 (VQA) 的指标 (AUC,子,BLEU,准确性) 的聚合分析.
- 使用 QUADAS-AI 检查清单评估报告质量和偏见.
主要成果:
- 确定了106项研究,其中94项被纳入了元分析.
- 聚合的AUC用于分类:0.86 (0.85-0.87).
- 聚合子对细分的系数为:0.73 (0.68-0.78).
- 报告生成的BLEU总得分:0.31 (0.20-0.43). 报告生成的BLEU总得分:0.31 (0.20-0.43). 报告生成的BLEU总得分:0.31 (0.20-0.43).
- 对于VQA的聚合精度:0.76 (0.71-0.81).
- 小组分析探讨了成像方式和出版年份之间的异质性.
结论:
- VLM在使用医学成像技术的计算机辅助临床诊断方面表现强.
- 通过更严格的报告标准来提高研究质量,以适应VLM研究挑战.
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