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AI and Aphasia in the Digital Age: A Critical Review.

作者信息

Privitera Adam John, Ng Siew Hiang Sally, Kong Anthony Pak-Hin, Weekes Brendan Stuart

机构信息

Centre for Research and Development in Learning, Nanyang Technological University, Singapore 637335, Singapore.

Institute for Pedagogical Innovation, Research, and Excellence, Nanyang Technological University, Singapore 637335, Singapore.

出版信息

Brain Sci. 2024 Apr 16;14(4):383. doi: 10.3390/brainsci14040383.


DOI:10.3390/brainsci14040383
PMID:38672032
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11047933/
Abstract

Aphasiology has a long and rich tradition of contributing to understanding how culture, language, and social environment contribute to brain development and function. Recent breakthroughs in AI can transform the role of aphasiology in the digital age by leveraging speech data in all languages to model how damage to specific brain regions impacts linguistic universals such as grammar. These tools, including generative AI (ChatGPT) and natural language processing (NLP) models, could also inform practitioners working with clinical populations in the assessment and treatment of aphasia using AI-based interventions such as personalized therapy and adaptive platforms. Although these possibilities have generated enthusiasm in aphasiology, a rigorous interrogation of their limitations is necessary before AI is integrated into practice. We explain the history and first principles of reciprocity between AI and aphasiology, highlighting how lesioning neural networks opened the black box of cognitive neurolinguistic processing. We then argue that when more data from aphasia across languages become digitized and available online, deep learning will reveal hitherto unreported patterns of language processing of theoretical interest for aphasiologists. We also anticipate some problems using AI, including language biases, cultural, ethical, and scientific limitations, a misrepresentation of marginalized languages, and a lack of rigorous validation of tools. However, as these challenges are met with better governance, AI could have an equitable impact.

摘要

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引用本文的文献

[1]
Treatment of aphasia in linguistically diverse populations: current and future directions.

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[2]
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本文引用的文献

[1]
Optimizing Communication in Ataxia: A Multifaceted Approach to Alternative and Augmentative Communication (AAC).

Cerebellum. 2024-10

[2]
The copenhagen cross-linguistic naming test (C-CLNT): Development and validation in a multicultural memory clinic population.

J Int Neuropsychol Soc. 2023-12

[3]
Shifting machine learning for healthcare from development to deployment and from models to data.

Nat Biomed Eng. 2022-12

[4]
Current Approaches to the Treatment of Post-Stroke Aphasia.

J Stroke. 2021-5

[5]
Predictors of Poststroke Aphasia Recovery: A Systematic Review-Informed Individual Participant Data Meta-Analysis.

Stroke. 2021-5

[6]
Considerations for a More Ethical Approach to Data in AI: On Data Representation and Infrastructure.

Front Big Data. 2020-9-2

[7]
Machine learning-based multimodal prediction of language outcomes in chronic aphasia.

Hum Brain Mapp. 2021-4-15

[8]
Aphasia in Alzheimer's Disease and Other Dementias (ADOD): Evidence From Chinese.

Am J Alzheimers Dis Other Demen. 2020

[9]
Constraint-induced aphasia therapy for patients with aphasia: A systematic review.

Int J Nurs Sci. 2020-5-28

[10]
The ethics of AI in health care: A mapping review.

Soc Sci Med. 2020-9

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