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Demystifying Large Language Models for Medicine: A Primer.

Qiao Jin¹, Nicholas Wan¹, Robert Leaman¹

¹National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.

|January 13, 2025

Summary

This summary is machine-generated.

This paper provides a guideline for healthcare professionals to effectively use large language models (LLMs) in clinical settings. It covers task formulation, model selection, prompt engineering, and deployment for safe and impactful AI integration in medicine.

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Area of Science:

Artificial Intelligence in Healthcare
Medical Informatics
Clinical Decision Support

Background:

Large language models (LLMs) offer transformative potential in healthcare.
LLMs can generate human-like responses and adapt to new medical tasks.
Applications include clinical documentation, patient-trial matching, and medical question answering.

Purpose of the Study:

To provide actionable guidelines for healthcare professionals to efficiently utilize LLMs.
To outline best practices for integrating LLMs into clinical workflows.
To facilitate the safe, reliable, and impactful application of AI in medicine.

Main Methods:

A structured, step-by-step methodology is proposed.
Phases include task formulation, LLM selection, prompt engineering, fine-tuning, and deployment.
Considerations for adapting LLMs to specialized medical tasks are reviewed.

Main Results:

The guideline addresses critical factors in identifying suitable healthcare tasks for LLMs.
Strategies for selecting appropriate LLMs based on task, data, and performance are discussed.
Methods for adapting LLMs, including prompt engineering and fine-tuning, are detailed.

Conclusions:

Effective LLM integration requires careful task alignment and model selection.
Prompt engineering and fine-tuning are key to specializing LLMs for medical use.
Deployment must address regulatory compliance, ethical considerations, and bias monitoring.