Improving the Safety and Trustworthiness of Medical AI via Multi-Agent Evaluation Loops

TL;DR

This paper presents a multi-agent iterative framework to improve the safety, ethical compliance, and reliability of medical AI models, demonstrating significant reductions in violations and risk levels across diverse clinical queries.

Contribution

Introduces a novel multi-agent refinement system that enhances medical LLM safety through structured, iterative alignment with ethical principles and risk assessments.

Findings

89% reduction in ethical violations

92% risk downgrade rate

Faster convergence with DeepSeek R1

Abstract

Large Language Models (LLMs) are increasingly applied in healthcare, yet ensuring their ethical integrity and safety compliance remains a major barrier to clinical deployment. This work introduces a multi-agent refinement framework designed to enhance the safety and reliability of medical LLMs through structured, iterative alignment. Our system combines two generative models - DeepSeek R1 and Med-PaLM - with two evaluation agents, LLaMA 3.1 and Phi-4, which assess responses using the American Medical Association's (AMA) Principles of Medical Ethics and a five-tier Safety Risk Assessment (SRA-5) protocol. We evaluate performance across 900 clinically diverse queries spanning nine ethical domains, measuring convergence efficiency, ethical violation reduction, and domain-specific risk behavior. Results demonstrate that DeepSeek R1 achieves faster convergence (mean 2.34 vs. 2.67 iterations), while Med-PaLM shows superior handling of privacy-sensitive scenarios. The iterative multi-agent loop achieved an 89% reduction in ethical violations and a 92% risk downgrade rate, underscoring the effectiveness of our approach. This study presents a scalable, regulator-aligned, and cost-efficient paradigm for governing medical AI safety.