FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights

TL;DR

FRAME is a novel framework that uses iterative refinement and structured feedback to improve the quality of automatically generated medical research papers, demonstrating significant gains and human-level quality.

Contribution

The paper introduces a tripartite agent architecture and a structured dataset construction method for enhancing medical paper generation.

Findings

Achieved 9.91% average improvement with DeepSeek V3

Generated papers comparable to human-authored works in quality

Effectively synthesized future research directions

Abstract

The automation of scientific research through large language models (LLMs) presents significant opportunities but faces critical challenges in knowledge synthesis and quality assurance. We introduce Feedback-Refined Agent Methodology (FRAME), a novel framework that enhances medical paper generation through iterative refinement and structured feedback. Our approach comprises three key innovations: (1) A structured dataset construction method that decomposes 4,287 medical papers into essential research components through iterative refinement; (2) A tripartite architecture integrating Generator, Evaluator, and Reflector agents that progressively improve content quality through metric-driven feedback; and (3) A comprehensive evaluation framework that combines statistical metrics with human-grounded benchmarks. Experimental results demonstrate FRAME's effectiveness, achieving significant improvements over conventional approaches across multiple models (9.91% average gain with DeepSeek V3, comparable improvements with GPT-4o Mini) and evaluation dimensions. Human evaluation confirms that FRAME-generated papers achieve quality comparable to human-authored works, with particular strength in synthesizing future research directions. The results demonstrated our work could efficiently assist medical research by building a robust foundation for automated medical research paper generation while maintaining rigorous academic standards.