The application of artificial intelligence methods to electronic patient records paves the way for large-scale analysis of multimodal data. Such population-wide data describing deep phenotypes composed of thousands of features are now being leveraged to create data-driven algorithms, which in turn has led to improved methods for early cancer detection and screening. Remaining challenges include establishment of infrastructures for prospective testing of such methods, ways to assess biases given the data, and gathering of sufficiently large and diverse datasets that reflect disease heterogeneities across populations. This Review provides an overview of artificial intelligence methods designed to detect cancer early, including key aspects of concern (eg, the problem of data drift—when the underlying health-care data change over time), ethical aspects, and discrepancies between access to cancer screening in high-income countries versus low-income and middle-income countries.

中文翻译：

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用于癌症检测和筛查的医疗记录的人工智能辅助数据挖掘


人工智能方法在电子病历中的应用为多模态数据的大规模分析铺平了道路。这种描述由数千个特征组成的深层表型的全人群数据现在被用来创建数据驱动的算法，这反过来又导致了早期癌症检测和筛查方法的改进。剩下的挑战包括建立用于此类方法的前瞻性测试的基础设施、根据数据评估偏差的方法，以及收集足够大和多样化的数据集来反映不同人群的疾病异质性。本综述概述了旨在早期发现癌症的人工智能方法，包括关注的关键方面（例如，数据漂移问题——当基础医疗保健数据随时间变化时）、伦理方面以及高收入国家与低收入和中等收入国家获得癌症筛查的机会之间的差异。