合成数据可以使医学研究受益——但必须认识到风险

IF 48.5 1区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Nature Pub Date : 2025-09-10 DOI:10.1038/d41586-025-02869-0

引用次数: 0

摘要

当真实数据不足时，人工生成的数据可以帮助训练人工智能模型，但需要更多地关注验证结果。当真实数据不足时，人工生成的数据可以帮助训练人工智能模型，但需要更多地关注验证结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Synthetic data can benefit medical research — but risks must be recognized

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Synthetic data can benefit medical research — but risks must be recognized

Artificially generated data can help to train AI models when real data are scant, but more focus is needed on validating the results. Artificially generated data can help to train AI models when real data are scant, but more focus is needed on validating the results.

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来源期刊

Nature 综合性期刊-综合性期刊

CiteScore

90.00

自引率

1.20%

发文量

3652

审稿时长

3 months

期刊介绍： Nature is a prestigious international journal that publishes peer-reviewed research in various scientific and technological fields. The selection of articles is based on criteria such as originality, importance, interdisciplinary relevance, timeliness, accessibility, elegance, and surprising conclusions. In addition to showcasing significant scientific advances, Nature delivers rapid, authoritative, insightful news, and interpretation of current and upcoming trends impacting science, scientists, and the broader public. The journal serves a dual purpose: firstly, to promptly share noteworthy scientific advances and foster discussions among scientists, and secondly, to ensure the swift dissemination of scientific results globally, emphasizing their significance for knowledge, culture, and daily life.