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arXiv · 2512.01045

Med-CRAFT: An Information System for Explainable and Configurable Construction of Multimodal Medical QA Datasets

Shenxi Liu·Kan Li·Mingyang Zhao·Yuhang Tian·Bin Li
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Abstract

Data-intensive artificial intelligence applications increasingly rely on large-scale, high-quality, explainable, and reproducible datasets, yet the construction of such datasets often remains labor-intensive, weakly traceable, and difficult to configure. This problem is particularly critical in multimodal medical scenarios, where each question-answer sample should be semantically consistent, grounded in visual and temporal evidence, and controllable in terms of reasoning complexity. To address these challenges, we propose Med-CRAFT, an information system for explainable and configurable construction of multimodal medical question answering datasets from instructional videos. Med-CRAFT organizes dataset construction as a provenance-aware pipeline that transforms raw medical instructional videos into structured operation knowledge graphs, evidence-grounded reasoning paths, and natural-language question-answer pairs.

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Shenxi Liu, Kan Li, Mingyang Zhao, Yuhang Tian, Bin Li. 2025-11-30. Med-CRAFT: An Information System for Explainable and Configurable Construction of Multimodal Medical QA Datasets. https://arxiv.org/abs/2512.01045

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