Ruiz Mejia Jose M, Rawat Danda B
Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA.
Healthcare (Basel). 2025 Jul 8;13(14):1649. doi: 10.3390/healthcare13141649.
With advancements in Generative Artificial Intelligence, various industries have made substantial efforts to integrate this technology to enhance the efficiency and effectiveness of existing processes or identify potential weaknesses. Context, however, remains a crucial factor in leveraging intelligence, especially in high-stakes sectors such as healthcare, where contextual understanding can lead to life-changing outcomes. This research aims to develop a practical medical multi-agent system framework capable of automating appointment scheduling and triage classification, thus improving operational efficiency in healthcare settings. We present , a multi-agent framework integrating established technologies: Gale-Shapley stable matching algorithm for optimal patient-provider allocation, knowledge graphs for semantic compatibility profiling, and specialized large language model-based agents. The framework is designed to emulate the collaborative decision making processes typical of medical teams. Our evaluation demonstrates that combining these components within a cohesive multi-agent architecture substantially enhances operational efficiency, task completeness, and contextual relevance in healthcare scheduling workflows. provides a practical, implementable blueprint for healthcare automation, addressing significant inefficiencies in real-world appointment scheduling and patient triage scenarios.
随着生成式人工智能的进步，各行业都做出了巨大努力来整合这项技术，以提高现有流程的效率和有效性，或识别潜在弱点。然而，上下文仍然是利用智能的关键因素，尤其是在医疗保健等高风险领域，上下文理解可能会带来改变生活的结果。本研究旨在开发一个实用的医疗多智能体系统框架，能够自动进行预约安排和分诊分类，从而提高医疗环境中的运营效率。我们提出了一个整合现有技术的多智能体框架：用于优化患者与提供者分配的盖尔-沙普利稳定匹配算法、用于语义兼容性分析的知识图谱以及基于专门大语言模型的智能体。该框架旨在模拟医疗团队典型的协作决策过程。我们的评估表明，在一个连贯的多智能体架构中结合这些组件，可显著提高医疗调度工作流程中的运营效率、任务完整性和上下文相关性。为医疗自动化提供了一个实用、可实施的蓝图，解决了现实世界中预约安排和患者分诊场景中的重大低效率问题。
