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Large language models (LLMs) can assist in generating guideline questions by analyzing online queries and direct generation, complementing expert input. This approach helped identify novel questions for the ARIA guidelines.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Guideline questions are traditionally developed by expert panels.
  • Identifying comprehensive and relevant questions is crucial for high-quality clinical guidelines.

Purpose of the Study:

  • To evaluate the utility of large language models (LLMs) in generating clinical guideline questions.
  • To explore two distinct LLM-driven approaches for question development: analyzing online search queries and direct LLM generation.
  • To provide practical insights and lessons learned from implementing these LLM-based methods.

Main Methods:

  • Assessed LLM performance in identifying questions from popular online search queries related to allergic rhinitis using Google Trends.
  • Tasked LLMs to directly generate guideline questions from patient and clinician perspectives.
  • Manually structured identified queries and LLM-generated questions into formal guideline questions.

Main Results:

  • LLM analysis of online queries identified 37 relevant questions, with 22 being novel and 2 prioritized by the panel.
  • Direct LLM generation produced 22 unique relevant questions, 11 novel, and 4 prioritized by the panel.
  • A total of 6 novel questions, not initially conceived by the panel, were prioritized for the 2024 ARIA guidelines.

Conclusions:

  • LLM-based approaches effectively support and augment the traditional development of clinical guideline questions.
  • These methods can identify novel and relevant questions, enhancing the comprehensiveness of guideline panels' work.
  • LLMs offer a valuable tool to complement expert-driven processes in guideline question formulation.