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Related Experiment Video

Updated: Sep 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Clinical Application of Large Language Models for Intervention Plan Development in Speech-Language Pathology.

Namhee Kim1, Mercy Homer1, Hyeju Jang2

  • 1California Baptist University, Riverside.

American Journal of Speech-Language Pathology
|May 22, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) tools show potential for speech-language pathology intervention plans, but current outputs range from needing improvement to meeting expectations. Detailed prompts enhance AI-generated intervention plan quality.

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Area of Science:

  • Speech-language pathology
  • Artificial intelligence in healthcare
  • Clinical decision support systems

Background:

  • Large language models (LLMs) are increasingly integrated into clinical writing tools.
  • Evaluating the efficacy of AI-generated content for clinical applications is crucial.

Purpose of the Study:

  • To assess the performance of six AI tools in generating speech and language intervention plans.
  • To identify the applications and limitations of AI in speech-language pathology intervention planning.

Main Methods:

  • A mixed-methods approach combining quantitative and qualitative analyses was employed.
  • Six AI tools were evaluated using three fictional pediatric speech and language disorder cases.
  • Two prompt types with varying specificity levels were used to generate AI outputs.

Main Results:

  • AI-generated intervention plans were rated from 'Needs Improvement' to 'Meets Expectations' for clinical competence.
  • Highly specific and structured prompts led to better AI output ratings compared to general prompts.
  • AI tools exhibited distinct strengths and weaknesses in supporting intervention plan development.

Conclusions:

  • Findings provide foundational data for responsible AI utilization in speech-language pathology.
  • Clinicians, educators, and students can leverage these insights for integrating AI into intervention planning.
  • Further research is needed to optimize AI tool performance and clinical integration.