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arXiv · 2505.03785

mAIstro: an open-source multi-agentic system for automated end-to-end development of radiomics and deep learning models for medical imaging

Eleftherios Tzanis·Michail E. Klontzas
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Abstract

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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Eleftherios Tzanis, Michail E. Klontzas. 2025-04-30. mAIstro: an open-source multi-agentic system for automated end-to-end development of radiomics and deep learning models for medical imaging. https://doi.org/10.1016/j.ejrai.2025.100044

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