一个系统的审查和诊断性能比较的元分析生成AI和医生之间的诊断性能比较
Hirotaka Takita1, Daijiro Kabata2, Shannon L Walston1,3
1Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
NPJ digital medicine
|March 23, 2025
概括
生成型人工智能 (AI) 在医学诊断方面显示出潜力,与整体医生的准确性相匹配,但不符合专家水平. 需要进一步的研究来理解人工智能.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 诊断性绩效评估的诊断性绩效评估是如何进行的
背景情况:
- 生成型人工智能 (AI) 在医学诊断方面表现有前途.
- 对AI诊断性能与医生进行全面评估是有限的.
- 了解人工智能的能力和局限性对于医疗整合至关重要.
研究的目的:
- 系统地审查和对生成AI诊断性能研究进行元分析.
- 将AI模型的诊断精度与医生 (整体,专家和非专家) 的诊断精度进行比较.
- 评估医疗诊断中生成人工智能的现状和潜力.
主要方法:
- 2018年6月至2024年6月期间发表的研究的系统审查和元分析.
- 包括验证用于诊断任务的生成AI模型的研究.
- 统计分析来比较人工智能性能与医生的表现.
主要成果:
- 对83项研究的分析显示,整体AI诊断准确率为52.1%.
- 人工智能模型和医生整体 (p=0.10) 或非专家医生 (p=0.93) 之间没有发现显著的性能差异.
- 人工智能模型的表现明显低于专家医生 (p=0.007),尽管一些模型略高于非专家.
结论:
- 生成型人工智能表现出有希望的诊断能力,其准确性因模型而异.
- 目前的人工智能模型尚未达到专家医生的可靠性水平.
- 人工智能有潜力提高医疗保健和医疗教育,当限制被理解时.
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