Nat Rev Clin Oncol, IF: 34.106
Google’s lung cancer AI: a promising tool that needs further validation
Researchers from Google AI have presented results obtained using a deep learning model for the detection of lung cancer in screening CT images. The authors report a level of performance similar to, or better than, that of radiologists. However, these claims are currently too strong. The model is promising but needs further validation and could only be implemented if screening guidelines were adjusted to accept recommendations from black- box proprietary AI systems.
来自谷歌AI的研究人员展示了使用深度学习模型在筛查CT图像中检测肺癌的结果。作者报告说，它们的表现水平与放射科医生相似，甚至更好。然而，这些论断还为时过早。该模型很有前景，但需要进一步验证，只有调整筛选指南，以接受黑箱专有人工智能系统的建议，才能实现。
A black-box AI-based system that overrules well established clinical guidelines that prompt radiologists to base management decisions on the size and growth rate of nodules is unlikely to be quickly accepted.
一个基于人工智能的黑箱系统不太可能很快被接受，因为它推翻了已经建立的临床指南，这些指南使放射科医生根据结节的大小和生长速度做出治疗决定。
Thus, if and how Google intends to translate this tool, one of several it has developed in recent years in the field of medical image analysis, into a product currently remains to be seen. All of the solutions developed by Google AI so far are proprietary. In the Nature Medicine letter, the authors state that their code has “dependencies on internal tooling, infrastructure and hardware, and its release is therefore not feasible. However, all experiments and implementation details are described in sufficient detail […] to allow independent replication”.
因此，谷歌是否以及打算如何推出这一工具（近年来在医学图像分析领域开发的几种产品之一）目前还有待观察。到目前为止，谷歌AI开发的所有解决方案都是专有的。在《自然医学》杂志的信中，作者指出他们的代码“依赖于内部工具、基础设施和硬件，因此其不大会发布”。然而，所有的实验和实现细节都被描述得足够详细[…]，以允许独立的复制。
This description, however, lacks many technical detailsthat might be crucial for obtaining a good level of performance. Therefore, we consider Google’s code availability statements as only paying lip service to the principle of reproducible science.
然而，这种描述缺乏许多技术细节，而这些细节对于获得良好的性能水平可能是至关重要的。因此，我们认为谷歌的代码可用性声明只是口头上支持可重复科学的原则。
（此文为转载）