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Artificial intelligence (AI)-driven technologies for managing pediatric speech and language therapy: A scoping review
Milad Dadgar1, Cathy Ennis2, Kesego Mokgosi1
1School of Computer Science, Technological University Dublin, Ireland.
Digital Health
|November 10, 2025
Summary
Artificial intelligence and machine learning are enhancing speech and language therapy (SLT) for children with speech sound disorders (SSDs). These AI-driven tools offer personalized therapy and improve diagnostic support for therapists.
Area of Science:
- Speech and Language Therapy
- Artificial Intelligence in Healthcare
- Pediatric Speech Disorders
Background:
- Speech sound disorders (SSDs) present a significant challenge due to limited accessible speech and language therapy (SLT) services for children.
- Technology, particularly AI and machine learning (ML), offers innovative solutions to support children, parents, and therapists in the SLT process.
- Automatic speech recognition and audio processing are key AI techniques driving advancements in SLT.
Purpose of the Study:
- To conduct a scoping review of studies utilizing AI and ML techniques for managing the SLT process in children with SSDs.
- To examine the role of automatic speech recognition and audio processing in current SLT practices.
- To identify the effectiveness and potential of AI-assisted SLT models.
Main Methods:
- A systematic search was performed on February 3, 2025, across five major databases (PubMed, Scopus, ScienceDirect, ACM Digital Library, IEEE Xplore).
- The search followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines.
- 30 out of 188 identified studies met the eligibility criteria for inclusion in the review.
Main Results:
- Studies predominantly employ deep neural networks, ML classifiers, acoustic features, and audio processing for SSD detection.
- AI applications effectively support therapists in diagnosing SSDs.
- Computer-based tools demonstrate higher engagement in children through personalized therapy plans and real-time feedback, aiding progress monitoring.
Conclusions:
- AI-assisted SLT models show significant effectiveness and potential in improving the SLT process.
- Gaps in current AI applications for SLT include data privacy, accessibility, and the need for robust clinical validation.
- Future research should focus on addressing these challenges to fully realize the benefits of AI in pediatric SLT.
Keywords:
Automated speech therapyaudio processingautomatic speech recognitionmachine learningspeech sound disorderMore Related Videos
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