Demystifying Large Language Models for Medicine: A Primer

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.