arXiv cs.AI· Giulio Colombini, Matteo Falcioni, Mario Massimo, Francesco Durazzi, Marianna Santoro, Paolo Tubertini, Mirko Degli Esposti, Armando Bazzani·· 14 小时前AI 评分27
EpidemIA：用 AI 多智能体系统让临床医生通过自然语言调用流行病学模型
Can AI Agents Interface Clinicians and Complex Models?
AI 导读
研究团队开发了 EpidemIA，一个无需训练的 AI 多智能体系统（MAS），让用户通过自然语言对话调用流行病学预测模型并完成统计分析。系统以本地部署的开源权重 LLM 作为两个子智能体的骨干，可在单 GPU 工作站运行，避免向第三方传输敏感数据。基于 IRCCS Sant'Orsola（博洛尼亚）提供的急诊就诊数据集，该系统在多个典型用例上验证了从简单统计可视化到复杂动力学模型预测的能力。
正文 · 原文
Abstract:Background: Contemporary healthcare institutions generate daily very large amounts of data, which contain highly useful information for the improvement of work organization, planning and overall efficiency. While models and techniques to extract these insights are well known and constantly developed in mathematical biology, complex systems physics and data science, the adoption of these techniques among Clinicians and Hospital Administrators remains limited, mainly due to language and methodological divides.
Objective: In this article we implement an AI Multi Agent System (MAS) that allows Users to use predictive models and make statistical analyses via natural language conversations.
Methods: We developed EpidemIA, a training-free MAS endowed with a modular suite of epidemiological models written by human experts, executable on a single-GPU workstation. To avoid relaying sensitive data to third parties, the system uses locally an open-weights LLM as backbone for its two subagents.
Results: Using a dataset of Emergency Department visits provided to us by IRCCS Sant'Orsola (Bologna) we test the model on a few typical use cases, demonstrating the possibility of performing both simpler statistical analyses and visualizations as well as more complicated epidemiological predictions based on dynamical models, through simple queries made in natural language.
Conclusions: While still being a proof-of-concept implementation, EpidemIA showcases the transformative capabilities of LLM based technologies as an enabling factor in the widespread adoption of complex dynamical models, which is at present hindered by their steep learning curve.
| Comments: | 21 pages, 7 figures |
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.11; J.3 |
| Cite as: | arXiv:2610.39681 [cs.MA] |
| (or arXiv:2610.39681v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39681 arXiv-issued DOI via DataCite |
Submission history
From: Giulio Colombini Ph.D. [view email]
[v1]
Thu, 29 Oct 2026 09:28:15 UTC (760 KB)
来源：arXiv cs.AI · arxiv.org