ClinicalReTrial: A Self-Evolving AI Agent for Clinical Trial Protocol Optimization

2026-01-01

arXiv.org

AI Summary

This work addresses the high failure rate of clinical trials stemming from suboptimal protocol design, a challenge inadequately tackled by existing AI methods that merely predict risk without offering actionable optimization. We propose the first framework that formulates clinical trial optimization as a self-evolving reinforcement learning problem, enabling automatic diagnosis, safety-constrained modification, and evaluation of protocols through a closed-loop iterative mechanism. The framework incorporates a hierarchical memory architecture to distill transferable redesign strategies across trials, integrating a failure diagnosis model, a constrained modification policy, and a simulation environment driven by reward-based optimization. Experimental results demonstrate that 83.3% of protocols were improved, with an average 5.7% increase in success probability, and the generated optimization strategies exhibit strong alignment with real-world protocol amendments.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Clinical trial failure remains a central bottleneck in drug development, where minor protocol design flaws can irreversibly compromise outcomes despite promising therapeutics. Although cutting-edge AI methods achieve strong performance in predicting trial success, they are inherently reactive for merely diagnosing risk without offering actionable remedies once failure is anticipated. To fill this gap, this paper proposes ClinicalReTrial, a self-evolving AI agent framework that addresses this gap by casting clinical trial reasoning as an iterative protocol redesign problem. Our method integrates failure diagnosis, safety-aware modification, 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 of protocol modifications and provides dense reward signals for continuous self-improvement. To support efficient exploration, the framework maintains 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%, and retrospective case studies demonstrate strong alignment between the discovered redesign strategies and real-world clinical trial modifications.

Problem

• clinical trial failure
• protocol design flaws
• trial success prediction
• actionable remedies
• clinical trial optimization

Innovation

• self-evolving AI agent
• clinical trial protocol optimization
• closed-loop reward-driven framework
• hierarchical memory
• iterative redesign