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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
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Enhancing Clinical Decision Support with Adaptive Iterative Self-Query Retrieval for Retrieval-Augmented Large

Srinivasagam Prabha1, Cesar A Gomez-Cabello1, Syed Ali Haider1

  • 1Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA.

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Summary

The Self-Query Retrieval (SQR) framework improves clinical decision support by automatically structuring and refining questions for large language models (LLMs). This enhances the accuracy and relevance of AI-generated medical guidance for physicians.

Keywords:
clinical decision supportdecision support systemslarge language modelsretrieval-augmented generationself-query retrieval

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Natural Language Processing

Background:

  • Retrieval-Augmented Generation (RAG) using large language models (LLMs) shows potential for clinical guidance but is limited by query quality.
  • Physician cognitive load can be reduced by AI, but requires accurate and structured information retrieval.
  • Current RAG systems struggle with unstructured or ambiguous clinical queries, impacting reliability.

Purpose of the Study:

  • To introduce and evaluate the Self-Query Retrieval (SQR) framework for enhancing clinical question clarity and structure.
  • To improve the accuracy, relevance, and retrieval quality of LLM-generated clinical guidance.
  • To assess the effectiveness of automated query refinement modules (PICOT, SPICE, IQR) within the SQR framework.

Main Methods:

  • Developed the adaptive SQR framework integrating PICOT, SPICE, and Iterative Query Refinement (IQR) modules.
  • Implemented SQR on the Gemini-1.0 Pro LLM and benchmarked with 30 postoperative rhinoplasty queries.
  • Evaluated response accuracy and relevance using a Likert scale and retrieval metrics (precision, recall, F1 score).

Main Results:

  • The full SQR pipeline achieved 87% accuracy and 100% relevance, significantly outperforming a non-refined RAG baseline (50% accuracy, 80% relevance).
  • SQR improved precision, recall, and F1 scores from 0.17, 0.39, 0.24 to 0.53, 1.00, 0.70, respectively.
  • PICOT-only and SPICE-only modules showed intermediate improvements, highlighting the benefit of the full pipeline.

Conclusions:

  • Automated query structuring and iterative refinement via SQR substantially enhance LLM-based clinical decision support.
  • The SQR framework demonstrates significant improvements in accuracy and relevance for clinical guidance.
  • SQR's model-agnostic design allows for broad applicability across medical specialties and data sources.