MedicalOS：基于 LLM 智能体的数字医疗操作系统
摘要
数十年来，电子健康记录等数字健康技术的进步在很大程度上简化了常规临床流程。然而，这些系统大多仍难以学习和使用：临床医生常常需要管理多个工具，对每个患者重复执行手动操作， navigate complicated UI trees to locate functions, and spend significant time on administration instead of caring for patients. The recent rise of large language model (LLM) based agents demonstrates exceptional capability in coding and computer operation, revealing the potential for humans to interact with operating systems and software not by direct manipulation, but by instructing agents through natural language. This shift highlights the need for an abstraction layer, an agent-computer interface, that translates human language into machine-executable commands. In digital healthcare, however, requires a more domain-specific abstractions that strictly follow trusted clinical guidelines and procedural standards to ensure safety, transparency, and compliance. To address this need, we present \textbf{MedicalOS}, a unified agent-based operational system designed as such a domain-specific abstract layer for healthcare. It translates human instructions into pre-defined digital healthcare commands, such as patient inquiry, history retrieval, exam management, report generation, referrals, treatment planning, that we wrapped as off-the-shelf tools using machine languages (e.g., Python, APIs, MCP, Linux). We empirically validate MedicalOS on 214 patient cases across 22 specialties, demonstrating high diagnostic accuracy and confidence, clinically sound examination requests, and consistent generation of structured reports and medication recommendations. These results highlight MedicalOS as a trustworthy and scalable foundation for advancing workflow automation in clinical practice.
引用
@article{arxiv.2509.11507,
title = {MedicalOS: An LLM Agent based Operating System for Digital Healthcare},
author = {Jared Zhu and Junde Wu},
journal= {arXiv preprint arXiv:2509.11507},
year = {2025}
}