ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1668399
This article is part of the Research TopicGenAI in Healthcare: Technologies, Applications and EvaluationView all 11 articles
Tracking priming-induced language recovery in aphasia with pre-trained language models
Provisionally accepted
- Purdue University, West Lafayette, United States
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This study explores the use of pre-trained language models (PLMs) in tracking priming treatment induced language recovery in aphasia. We evaluate PLM-derived surprisals, the negative log-probabilities of a word or a sequence of words calculated by a PLM given its preceding context, as a continuous and interpretable measure of treatment-induced language change. We found that surprisal scores decreased following structural priming treatment, especially in participants with more severe sentence production impairments. We also introduce a prompting-based pipeline for clinical classification tasks. It achieved promising results in classifying aphasia sentence correctness (F1 = 0.967) and detecting error categories in aphasia (accuracy = 0.846). Such use of PLMs for modeling, tracking, and automatically classifying language recovery in aphasia represents a promising deployment of GenAI in a clinical rehabilitation setting. Together, our PLM-based analyses offer a practical approach for modeling language rehabilitation, tracking not only language structure but also individual change over time in clinical contexts. (Clinical Trial registration No: NTC05415501).
Keywords: GenAI, Large language models, language rehabilitation, aphasia recovery, structural priming astreatment, Prompt Engineering, Aphasia, automatic clinical assessment
Received: 17 Jul 2025; Accepted: 15 Oct 2025.
Copyright: © 2025 Cong and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jiyeon Lee, lee1704@purdue.edu
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