Machine learning models in post-stroke aphasia: a scoping review

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The review systematically examines machine learning applications in post-stroke aphasia, highlighting their potential for clinical implementation.
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Nineteen publications were analyzed, revealing that supervised machine learning techniques like random forests and neural networks are commonly used.
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Machine learning models assist in diagnosing, categorizing, and predicting outcomes for aphasia patients, enhancing rehabilitation strategies.
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The study emphasizes the need for multi-center, multi-modal data and external validation to improve the reliability of machine learning models.
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This scoping review contributes to understanding the landscape of machine learning in aphasia, guiding future research and clinical applications.

Frontiers In Neurology

by Xiaoxue Li, Hengjie Song, Ningjing Guo, Congmin Kang, Xiaoyan Gong, Xinyu Ji, Jie Zheng
May 7, 2026

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