Large Language Models (LLMs) and multi-agent systems have shown impressive capabilities in natural language tasks but face challenges in clinical trial applications, primarily due to limited access to external knowledge. Recognizing the potential of advanced clinical trial tools that aggregate and predict based on the latest medical data, we propose an integrated solution to enhance their accessibility and utility. We introduce Clinical Agent System (CT-Agent), a Clinical multi-agent system designed for clinical trial tasks, leveraging GPT-4, multi-agent architectures, LEAST-TO-MOST, and ReAct reasoning technology. This integration not only boosts LLM performance in clinical contexts but also introduces novel functionalities. Our system autonomously manages the entire clinical trial process, demonstrating significant efficiency improvements in our evaluations, which include both computational benchmarks and expert feedback.

翻译：摘要：大语言模型（LLMs）与多智能体系统在自然语言任务中展现出显著能力，但在临床试验应用中面临挑战，主要受限于对外部知识的获取不足。基于对能整合最新医学数据并进行预测的先进临床试验工具的潜力认知，我们提出一种集成解决方案以增强其可访问性与实用性。本文提出临床智能体系统（CT-Agent）——一种专为临床试验任务设计的临床多智能体系统，该系统融合了GPT-4、多智能体架构、LEAST-TO-MOST与ReAct推理技术。这一集成不仅提升了LLM在临床场景中的性能，还引入了全新的功能。我们的系统能自主管理整个临床试验流程，在包含计算基准测试与专家反馈的评估中展现出显著的效率提升。