无需编码且保护隐私的智能框架，自动完成临床研究全流程
Coding-Free and Privacy-Preserving Agentic Framework for Data-Driven Clinical Research
- 用自然语言驱动研究流程，结合大模型与模块化工具
- 在三组数据上完成规划与文书，报告完整度达96%（大模型评估）
- 适合临床医生和外部研究者快速开展隐私保护的研究
临床数据驱动研究需具备医学知识、编程能力、患者数据访问权限及大量文档，形成障碍并减缓研究进度。为此，我们开发了临床智能研究系统（CARIS），自动化完成研究规划、文献检索、队列构建、机构审查委员会（IRB）文档、Vibe机器学习及报告生成，并支持人工介入优化。CARIS通过模型上下文协议（MCP）集成大语言模型（LLMs）与模块化工具，实现无代码自然语言操作，用户仅可访问输出结果。我们在三个异构数据集上评估，系统在四轮迭代内完成规划与IRB文档，支持Vibe ML，生成报告。大模型评估显示报告完整度达96%，人工评估为82%。CARIS有望降低文档负担与技术门槛，加速公共与私有数据环境中的数据驱动临床研究。
原文摘要 · Abstract (English)
Clinical data-driven research requires clinical expertise, programming skills, access to patient data, and extensive documentation, creating barriers and slowing the pace for clinicians and external researchers. To address this, we developed the Clinical Agentic Research Intelligence System (CARIS) that automates the workflow: research planning, literature search, cohort construction, Institutional Review Board (IRB) documentation, Vibe Machine Learning (ML), and report generation, with human-in-the-loop refinement. CARIS integrates Large Language Models (LLMs) with modular tools through the Model Context Protocol (MCP), enabling natural language-driven research without coding while allowing users to access only outputs. We evaluated CARIS on three heterogeneous datasets with distinct clinical tasks, where it completed planning and IRB documentation within four iterations, supported Vibe ML, and generated reports, achieving 96% completeness in LLM-based evaluation and 82% in human evaluation. CARIS demonstrates potential to reduce documentation burden and technical barriers, accelerating data-driven clinical research across public and private data environments.
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