mAIstro让医生零代码自动开发医学影像AI模型
mAIstro: an open-source multi-agentic system for automated end-to-end development of radiomics and deep learning models for medical imaging
- 用自然语言指挥多个智能体完成从数据到模型的全流程
- 在16个公开数据集上成功执行全部任务，结果可解释
- 适合临床研究者快速搭建可复现的AI分析流程
基于大语言模型的智能体系统在医疗AI复杂流程自动化方面展现出巨大潜力。我们提出mAIstro，一个开源的自主多智能体框架，可实现医学AI模型的端到端开发与部署。该系统通过自然语言接口，协调探索性数据分析、放射组学特征提取、图像分割、分类与回归等任务，用户无需编程。基于模块化架构，mAIstro支持开源与闭源LLM，在涵盖多种成像模态、解剖区域和数据类型的16个公开数据集上进行了评估。所有任务均成功执行，生成了可解释的输出与验证过的模型。本工作首次实现了跨多样化医疗应用的数据分析、AI模型开发与推理的统一，为临床与科研AI集成提供了可复现、可扩展的基础。代码已开源：https://github.com/eltzanis/mAIstro
原文摘要 · Abstract (English)
Agentic systems built on large language models (LLMs) offer promising capabilities for automating complex workflows in healthcare AI. We introduce mAIstro, an open-source, autonomous multi-agentic framework for end-to-end development and deployment of medical AI models. The system orchestrates exploratory data analysis, radiomic feature extraction, image segmentation, classification, and regression through a natural language interface, requiring no coding from the user. Built on a modular architecture, mAIstro supports both open- and closed-source LLMs, and was evaluated using a large and diverse set of prompts across 16 open-source datasets, covering a wide range of imaging modalities, anatomical regions, and data types. The agents successfully executed all tasks, producing interpretable outputs and validated models. This work presents the first agentic framework capable of unifying data analysis, AI model development, and inference across varied healthcare applications, offering a reproducible and extensible foundation for clinical and research AI integration. The code is available at: https://github.com/eltzanis/mAIstro
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