缓解基于人工智能的神经疾病检测中数据异质性的策略。

IF 14.7 1区医学 Q1 NEUROSCIENCES

Neuron Pub Date : 2025-04-16 Epub Date: 2025-03-03 DOI:10.1016/j.neuron.2025.01.028

Matthew Leming, Kyungsu Kim, Rose Bruffaerts, Hyungsoon Im

引用次数: 0

摘要

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

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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来源期刊

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.