人工智能标签反循环的发现:临床实施的人工智能算法的成功如何为算法再训练创造了意想不到的挑战
Patrick L Day1, Mikolaj A Wieczorek2, Denise Rokke1
1Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Clinical chemistry
|February 2, 2026
概括
实验室测试中的人工智能 (AI) 可以创建一个"AI标签反循环",对模型再培训和评估产生负面影响. 仔细的人类监督和验证对于在临床环境中可靠的AI系统性能至关重要.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的人工智能
- 实验室医学 实验室医学
背景情况:
- 人工智能 (AI) 增强了实验室测试,提高了质量和效率.
- 临床人工智能实施可能会导致模型再培训的意外挑战.
- 这项研究在人工智能增强的结石组成测试中确定了一个"AI标签反循环".
研究的目的:
- 描述AI标签反循环的发现.
- 分析其对模型再培训和评估的影响.
- 为临床AI系统提出缓解策略.
主要方法:
- 评估了人工智能增强结石组成测试的两个版本 (V1和V2).
- V2使用的数据量是初始V1模型的6倍.
- 在三个数据集上评估了性能:人工智能影响的标签,人工智能前的人类标签和人为纠正的标签.
主要成果:
- 尽管有更多的训练数据,但V2对人工智能影响的数据的一致性比V1低10%.
- 在人工智能前的人类标记数据上,V1和V2的性能相似.
- 在人类纠正的数据上,V2的表现优于V1,特别是在罕见的结石类型上,揭示了反循环.
结论:
- 将AI整合到临床实践中可以影响测试结果,使AI模型开发和评估复杂化.
- 持续的人类注释和细致的验证集构建至关重要.
- 这些策略确保可靠的AI性能评估,并支持安全的临床AI演变.
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