The age of foundation models

IF 81.1 1区 医学 Q1 ONCOLOGY Nature Reviews Clinical Oncology Pub Date : 2024-09-05 DOI:10.1038/s41571-024-00941-8
Jana Lipkova, Jakob Nikolas Kather
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Abstract

The development of clinically relevant artificial intelligence (AI) models has traditionally required access to extensive labelled datasets, which inevitably centre AI advances around large centres and private corporations. Data availability has also dictated the development of AI applications: most studies focus on common cancer types, and leave rare diseases behind. However, this paradigm is changing with the advent of foundation models, which enable the training of more powerful and robust AI systems using much smaller datasets.

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基础模型时代
传统上,开发临床相关的人工智能(AI)模型需要获取大量标记数据集,这就不可避免地使人工智能的发展集中在大型中心和私营企业周围。数据的可用性也决定了人工智能应用的发展:大多数研究都集中在常见癌症类型上,而将罕见疾病抛在脑后。然而,随着基础模型的出现,这种模式正在发生变化,它可以使用小得多的数据集来训练更强大、更稳健的人工智能系统。
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来源期刊
CiteScore
99.40
自引率
0.40%
发文量
114
审稿时长
6-12 weeks
期刊介绍: Nature Reviews publishes clinical content authored by internationally renowned clinical academics and researchers, catering to readers in the medical sciences at postgraduate levels and beyond. Although targeted at practicing doctors, researchers, and academics within specific specialties, the aim is to ensure accessibility for readers across various medical disciplines. The journal features in-depth Reviews offering authoritative and current information, contextualizing topics within the history and development of a field. Perspectives, News & Views articles, and the Research Highlights section provide topical discussions, opinions, and filtered primary research from diverse medical journals.
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