Strategies for mitigating data heterogeneities in AI-based neuro-disease detection.

IF 14.7 1区 医学 Q1 NEUROSCIENCES Neuron Pub Date : 2025-02-25 DOI:10.1016/j.neuron.2025.01.028
Matthew Leming, Kyungsu Kim, Rose Bruffaerts, Hyungsoon Im
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引用次数: 0

Abstract

In this NeuroView, we discuss challenges and best practices when dealing with disease-detection AI models that are trained on heterogeneous clinical data, focusing on the interrelated problems of model bias, causality, and rare diseases.

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在本期 NeuroView 中,我们将讨论在处理根据异构临床数据训练的疾病检测人工智能模型时所面临的挑战和最佳实践,重点关注模型偏差、因果关系和罕见病等相互关联的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neuron
Neuron 医学-神经科学
CiteScore
24.50
自引率
3.10%
发文量
382
审稿时长
1 months
期刊介绍: Established as a highly influential journal in neuroscience, Neuron is widely relied upon in the field. The editors adopt interdisciplinary strategies, integrating biophysical, cellular, developmental, and molecular approaches alongside a systems approach to sensory, motor, and higher-order cognitive functions. Serving as a premier intellectual forum, Neuron holds a prominent position in the entire neuroscience community.
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