Access to the most up-to-date information on medical countermeasures is important for the research and development of effective treatments for viruses and marine toxins. However, there is a lack of comprehensive databases that curate data on viruses and marine toxins, making decisions on medical countermeasures slow and difficult. In this work, we employ two large language models (LLMs) of ChatGPT and Grok to design two comprehensive databases of therapeutic countermeasures for five viruses of Lassa, Marburg, Ebola, Nipah, and Venezuelan equine encephalitis, as well as marine toxins. With high-level human-provided inputs, the two LLMs identify public databases containing data on the five viruses and marine toxins, collect relevant information from these databases and the literature, iteratively cross-validate the collected information, and design interactive webpages for easy access to the curated, comprehensive databases. Notably, the ChatGPT LLM is employed to design agentic AI workflows (consisting of two AI agents for research and decision-making) to rank countermeasures for viruses and marine toxins in the databases. Together, our work explores the potential of LLMs as a scalable, updatable approach for building comprehensive knowledge databases and supporting evidence-based decision-making.

翻译：获取最新医疗防治对策信息对于研发针对病毒和海洋毒素的有效治疗方案至关重要。然而，目前缺乏系统整合病毒与海洋毒素数据的综合数据库，导致医疗对策决策缓慢且困难。本研究利用ChatGPT和Grok两种大语言模型，构建了针对拉沙病毒、马尔堡病毒、埃博拉病毒、尼帕病毒、委内瑞拉马脑炎病毒及海洋毒素的五大病毒治疗对策综合数据库。在人类提供高水平输入的前提下，两种大语言模型识别包含五种病毒及海洋毒素数据的公共数据库，从这些数据库及文献中收集相关信息，通过迭代交叉验证确保数据准确性，并设计交互式网页以便便捷访问经整合的综合数据库。值得注意的是，本研究利用ChatGPT大语言模型设计了智能体人工智能工作流（包含两个分别负责研究与决策的人工智能智能体），用于对数据库中的病毒与海洋毒素防治对策进行排序。综上，本研究探索了大语言模型作为可扩展、可更新的方法构建综合知识库并支持循证决策的潜力。