医学研究中的数据解放与众包：集体智能与人工智能的交叉。

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Radiology-Artificial Intelligence Pub Date : 2024-01-01 DOI:10.1148/ryai.230006

Jefferson R Wilson, Luciano M Prevedello, Christopher D Witiw, Adam E Flanders, Errol Colak

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引用次数: 0

摘要

尽管全球产生的医疗数据量呈指数级增长，但对于那些通过应用人工智能等先进分析技术来开发更好的医疗解决方案的人来说，这些数据中的大部分都无法获得。数据解放和众包代表了两种不同但相互关联的方法，可用于弥合现有的数据孤岛并加快国际创新步伐。在本文中，我们将在医学人工智能研究的背景下审视这些概念，总结它们的潜在益处，找出潜在隐患，并最终为它们在未来的推广使用提供依据。本文提供了一个利用国际医学影像数据集开展众包竞赛的实例。关键词人工智能、数据解放、众包 © RSNA, 2023.

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Data Liberation and Crowdsourcing in Medical Research: The Intersection of Collective and Artificial Intelligence.

In spite of an exponential increase in the volume of medical data produced globally, much of these data are inaccessible to those who might best use them to develop improved health care solutions through the application of advanced analytics such as artificial intelligence. Data liberation and crowdsourcing represent two distinct but interrelated approaches to bridging existing data silos and accelerating the pace of innovation internationally. In this article, we examine these concepts in the context of medical artificial intelligence research, summarizing their potential benefits, identifying potential pitfalls, and ultimately making a case for their expanded use going forward. A practical example of a crowdsourced competition using an international medical imaging dataset is provided. Keywords: Artificial Intelligence, Data Liberation, Crowdsourcing © RSNA, 2023.

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来源期刊

Radiology-Artificial Intelligence

CiteScore

16.20

自引率

1.00%

发文量

期刊介绍： Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.