mlsanityData to 14 Oct 2026

Retrieval-Augmented Generation of Pediatric Speech-Language Pathology vignettes: A Proof-of-Concept Study

arXiv 2511.08600 ↗First posted 1 Nov 2025cs.CL cs.HCcited by 0

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

Clinical vignettes are essential educational tools in speech-language pathology (SLP), but manual creation is time-intensive. While general-purpose large language models (LLMs) can generate text, they lack domain-specific knowledge, leading to hallucinations and requiring extensive expert revision. This study presents a proof-of-concept system integrating retrieval-augmented generation (RAG) with curated knowledge bases to generate pediatric SLP case materials. A multi-model RAG-based system was prototyped integrating curated domain knowledge with engineered prompt templates, supporting five commercial (GPT-4o, Claude 3.5 Sonnet, Gemini 2.5 Pro) and open-source (Llama 3.2, Qwen 2.5-7B) LLMs. Seven test scenarios spanning diverse disorder types and grade levels were systematically designed. Generated cases underwent automated quality assessment using a multi-dimensional rubric evaluating structural completeness, internal consistency, clinical appropriateness, and IEP goal/session note quality. This proof-of-concept demonstrates technical feasibility for RAG-augmented generation of pediatric SLP vignettes. Commercial models showed marginal quality advantages, but open-source alternatives achieved acceptable performance, suggesting potential for privacy-preserving institutional deployment. Integration of curated knowledge bases enabled content generation aligned with professional guidelines. Extensive validation through expert review, student pilot testing, and psychometric evaluation is required before educational or research implementation. Future applications may extend to clinical decision support, automated IEP goal generation, and clinical reflection training.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 7 of its 56 references that are in the map. First posted on arXiv 1 Nov 2025.

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7 of 56 references.

References · 7

  1. 2023A Survey of Large Language Modelscited by 2,576
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  3. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  4. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  5. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 431
  6. 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 178
  7. 2021Controlled Text Generation as Continuous Optimization with Multiple Constraintscited by 70

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