Abstract
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.
Abstract (translated)
大语言模型（LLMs）和多代理系统在自然语言任务中表现出令人印象深刻的 capabilities，但在临床试验应用中面临挑战，主要原因是对外部知识的访问有限。认识到基于最新医疗数据的聚合和预测的高级临床试验工具的潜在能力，我们提出了一个集成解决方案，以增强其可访问性和实用性。我们引入了临床代理系统（CT-Agent），这是一种专为临床试验任务设计的临床多代理系统，利用了GPT-4、多代理架构、LEAST-TO-MOST和ReAct推理技术。这一集成不仅提高了LLM在临床情境中的性能，还引入了新的功能。我们的系统自主管理整个临床试验过程，证明了我们在评估中实现显著的效率改进，包括计算基准和专家反馈。
URL
https://arxiv.org/abs/2404.14777
