This research introduces a novel framework that automatically optimizes LLM prompts for specialized medical applications without requiring extensive model fine-tuning or retraining.

Key Innovations:

• Uses textual gradients to systematically improve prompt performance for medical applications
• Focuses on optimizing specialized medical knowledge, particularly in nephrology
• Evaluated on multiple medical benchmarks including MedQA and PubMedQA
• Demonstrates how general foundation models can be effectively leveraged for specialized medical tasks

Why It Matters: AutoMedPrompt represents a significant advancement for healthcare AI, enabling more accurate medical responses from foundation models without the computational cost of retraining. This approach can potentially accelerate the adoption of AI in clinical decision support while maintaining domain-specific accuracy.

AutoMedPrompt: A New Framework for Optimizing LLM Medical Prompts Using Textual Gradients