Evidence map›Paper›PMID 38672032›Full record

ReviewBrain sciences2024

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

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

Abstract readReview
In one paragraph

Review in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Computerized Platform in Hindi Language: Exploring Its Use in Aphasia Screening.IEEE journal of translational engineering in health and medicine · 2026
    Article
  3. Article
  4. Review
  5. Artificial Intelligence in Communication Sciences and Disorders: Introduction to the Forum.Journal of speech, language, and hearing research : JSLHR · 2024
    Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Adam John PriviteraCentre for Research and Development in Learning, Nanyang Technological University, Singapore 637335, Singapore.ORCID 0000-0003-1486-8954
Siew Hiang Sally NgCentre for Research and Development in Learning, Nanyang Technological University, Singapore 637335, Singapore.
Anthony Pak-Hin KongAcademic Unit of Human Communication, Learning, and Development, The University of Hong Kong, Pokfulam, Hong Kong.ORCID 0000-0002-6211-0358
Brendan Stuart WeekesFaculty of Education, The University of Hong Kong, Pokfulam, Hong Kong.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

aphasiaartificial intelligencecommunication disordersdeep learningpersonalized therapy

Identifiers

PMID38672032
PMCPMC11047933

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.