An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications.
Claire Cordella, Manuel J Marte, Hantian Liu and 1 others
PMID 40786002WHAT IT FOUND
This primer explains machine learning concepts for speech-language pathologists, reviewing how algorithms predict aphasia severity and therapy response.
It highlights that while models can outperform clinicians in specific classification tasks, they remain research tools that require careful interpretation and cannot yet replace clinical judgment.
Key findings
01In a review of primary progressive aphasia diagnosis, a deep neural network model achieved 80% classification accuracy, which was higher than the 67% mean accuracy of three speech-language pathologists listening to the same speech samples.
02Machine learning models can identify clinically meaningful patterns, such as predicting aphasia severity from neuroimaging data with correlations between 0.79 and 0.88, or discovering patient subgroups based on simple speech metrics like total word count.
03A major barrier to clinical use is the 'black box' problem, where complex models lack transparency, requiring specific interpretability methods like SHAP to understand which patient features drive predictions.
STILL TO COME
How it was doneWhat they foundWhat it means for SLPs
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What it does not show
This is a narrative review, not a systematic review or meta-analysis, so the selection of exemplar papers is subjective and may not represent the full breadth of ML research. The cited studies are primarily research-stage and have not been clinically validated for routine use. The review focuses heavily on aphasiology, so applications to other speech-language pathology domains are not detailed. The paper does not report new primary data, so findings are summaries of other studies' results.
Declared interests
The text does not contain an explicit conflict of interest statement, but it is published in a professional association journal. It references commercial therapy apps (e.g., Constant Therapy) and proprietary algorithms, noting their existence but not evaluating their efficacy independently.
The easy way to misread this
Do not assume that machine learning models are currently ready for clinical decision-making. The high accuracy rates cited (e.g., 80% classification) are from controlled research studies using specific datasets, and models often fail to generalize to new populations or have lower accuracy for certain subgroups. Furthermore, the 'black box' nature of many models means clinicians cannot yet fully trust or understand the rationale behind individual predictions without specialized interpretability tools.