ArticleScientific reports2024
Clinical efficacy of pre-trained large language models through the lens of aphasia.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring the Effectiveness of Technology-Based Interventions in Aphasia Rehabilitation: A Systematic Review.International journal of language & communication disordersPooled it
- Surprisal from Large Language Models as an individual difference: Capturing the individual regardless of the text answer.Behavior research methods · 2026Article
- Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Large Language Models in Clinical Neurology: A Systematic Review.Research square · 2026Article
- Evaluating the utility of large language models for detecting and simulating language dysfunction.Frontiers in artificial intelligence · 2026Article
- Large Language Models' Ability to Assess Main Concepts in Story Retelling: A Proof-of-Concept Comparison of Human Versus Machine Ratings.American journal of speech-language pathology · 2025Article
- Reconstructing impaired language using generative AI for people with aphasia.Scientific reports · 2025Article
- Enhanced large language models for effective screening of depression and anxiety.Communications medicine · 2025Article
- Navigating the Complexity of Bilingual Aphasia: Current Insights and Future Directions.Brain sciences · 2025Review
- ABCD: A Simulation Method for Accelerating Conversational Agents With Applications in Aphasia Therapy.Journal of speech, language, and hearing research : JSLHR · 2025Article
- Language neuroscience in the operating room: neurosurgical considerations for multilingual brain tumor patients.Frontiers in oncology · 2025Review
- Tracking priming-induced language recovery in aphasia with pre-trained language models.Frontiers in artificial intelligence · 2025Article
- Treatment of aphasia in linguistically diverse populations: current and future directions.Frontiers in psychology · 2025Review
Corrections and comments
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Authors and funding
3 authors.
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Abstract
The rapid development of large language models (LLMs) motivates us to explore how such state-of-the-art natural language processing systems can inform aphasia research. What kind of language indices can we derive from a pre-trained LLM? How do they differ from or relate to the existing language features in aphasia? To what extent can LLMs serve as an interpretable and effective diagnostic and measurement tool in a clinical context? To investigate these questions, we constructed predictive and correlational models, which utilize mean surprisals from LLMs as predictor variables. Using AphasiaBank archived data, we validated our models' efficacy in aphasia diagnosis, measurement, and prediction. Our finding is that LLMs-surprisals can effectively detect the presence of aphasia and different natures of the disorder, LLMs in conjunction with the existing language indices improve models' efficacy in subtyping aphasia, and LLMs-surprisals can capture common agrammatic deficits at both word and sentence level. Overall, LLMs have potential to advance automatic and precise aphasia prediction. A natural language processing pipeline can be greatly benefitted from integrating LLMs, enabling us to refine models of existing language disorders, such as aphasia.
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