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Large language models (LLMs) can accelerate the creation of evidence-based practice guidelines by rapidly synthesizing information. This study shows LLMs

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Clinical Practice Guidelines
  • Health Informatics

Background:

  • Formulating evidence-based practice guidelines is complex and requires significant expertise.
  • Artificial intelligence (AI) offers potential to streamline guideline development.
  • This study investigates the use of five large language models (LLMs) for generating guideline recommendations.

Purpose of the Study:

  • To evaluate the feasibility of using LLMs to generate recommendations from structured evidence.
  • To assess the concordance of recommendations generated by different LLMs.
  • To explore the potential of AI in accelerating guideline formulation.

Main Methods:

  • Validated general and specific prompts were developed.
  • Evidence-based health and lifestyle guidelines were sourced from PubMed.
  • One recommendation and its supporting evidence were randomly extracted from each guideline.
  • Five LLMs (ChatGPT-3.5, Claude-3 sonnet, Bard, ChatGLM-4, Kimi) generated recommendations based on the provided evidence.

Main Results:

  • ChatGPT-3.5 excelled at evidence synthesis and novel insight generation.
  • Bard consistently aligned with existing guideline principles.
  • Claude focused on evidence analysis and reducing irrelevant information.
  • ChatGLM-4 balanced evidence extraction and guideline adherence.
  • Kimi generated concise, targeted recommendations.
  • Average recommendation consistency ranged from 50% to 91.7%.

Conclusions:

  • LLMs show significant potential for accelerating the development of evidence-based recommendations.
  • LLMs can efficiently extract and synthesize information from structured evidence.
  • Generated recommendations align well with the supporting evidence.