Haue Amalie Dahl, Hjaltelin Jessica Xin, Holm Peter Christoffer, Placido Davide, Brunak S Ren
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Copenhagen University Hospital Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Lancet Oncol. 2024 Dec;25(12):e694-e703. doi: 10.1016/S1470-2045(24)00277-8.
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.
将人工智能方法应用于电子病历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度表型的全人群数据现在正被用于创建数据驱动的算法，这反过来又带来了改进的早期癌症检测和筛查方法。剩下的挑战包括建立用于前瞻性测试此类方法的基础设施、鉴于数据评估偏差的方法，以及收集足够大且多样的数据集以反映不同人群中的疾病异质性。本综述概述了旨在早期检测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在癌症筛查可及性方面的差异。
