ClinicalReTrial: Clinical Trial Redesign with Self-Evolving Agents

TL;DR

ClinicalReTrial is a multi-agent AI system that iteratively redesigns clinical trial protocols to improve success rates, utilizing failure diagnosis, safety considerations, and hierarchical memory for continuous self-improvement.

Contribution

This paper introduces ClinicalReTrial, a novel multi-agent framework that automates clinical trial protocol redesign with a hierarchical memory and reward-driven optimization.

Findings

Improves 83.3% of trial protocols with a 5.7% success probability gain.

Enables low-cost evaluation with approximately $0.12 per trial.

Demonstrates alignment of redesign strategies with real-world modifications.

Abstract

Clinical trials constitute a critical yet exceptionally challenging and costly stage of drug development (\$2.6B per drug), where protocols are encoded as complex natural language documents, motivating the use of AI systems beyond manual analysis. Existing AI methods accurately predict trial failure, but do not provide actionable remedies. To fill this gap, this paper proposes ClinicalReTrial, a multi-agent system that formulates clinical trial optimization as an iterative redesign problem on textural protocols. Our method integrates failure diagnosis, safety-aware modifications, and candidate evaluation in a closed-loop, reward-driven optimization framework. Serving the outcome prediction model as a simulation environment, ClinicalReTrial enables low-cost evaluation and dense reward signals for continuous self-improvement. We further propose a hierarchical memory that captures iteration-level feedback within trials and distills transferable redesign patterns across trials. Empirically, ClinicalReTrial improves $83.3\%$ of trial protocols with a mean success probability gain of $5.7\%$ with negligible cost (\$0.12 per trial). Retrospective case studies demonstrate alignment between the discovered redesign strategies and real-world clinical trial modifications. The code is anonymously available at: https://github.com/xingsixue123/ClinicalFailureReasonReTrial.