Artificial intelligence (AI) algorithms are used in an increasing range of aspects of our lives. In particular, medical applications of AI are being developed and deployed, including many in image analysis. Deep learning methods, which have recently proved successful in image classification, rely on large volumes of clinical data generated by healthcare institutions. Such data is collected from their served populations. In this opinion article, using digital mammographic screening as an example, we briefly consider the background to AI development and some issues around its deployment. We highlight the importance of high quality clinical data as fundamental to these technologies, and question how the ownership of resultant tools should be defined. Though many of the ethical issues concerning the development and use of medical AI technologies continue to be discussed, the value of the data on which they rely remains a subject that is seldom considered. This potentially controversial issue can and should be addressed in a way which is beneficial to all parties, particularly the population in general and the patients we serve.

人工智能 (AI) 算法被用于我们生活中越来越多的方面。特别是，人工智能的医疗应用正在开发和部署中，包括许多图像分析。最近证明在图像分类方面取得成功的深度学习方法依赖于医疗机构生成的大量临床数据。这些数据是从他们服务的人群中收集的。在这篇观点文章中，我们以数字乳腺 X 线筛查为例，简要介绍了人工智能发展的背景以及围绕其部署的一些问题。我们强调高质量临床数据作为这些技术基础的重要性，并质疑应如何定义结果工具的所有权。尽管关于医疗人工智能技术的开发和使用的许多伦理问题仍在继续讨论，但它们所依赖的数据的价值仍然是一个很少被考虑的主题。这个潜在有争议的问题可以而且应该以对所有各方都有利的方式解决，特别是一般人群和我们所服务的患者。