MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging

• URL: http://arxiv.org/abs/2604.13756v1
• Date: Wed, 15 Apr 2026 11:41:20 GMT
• Title: MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging
• Abstract summary: We propose a paradigm shift toward multidimensional, fine-grained and in-depth evaluation.<n>We benchmark 33 MLLMs, textitLingshu-32B achieve top-tier performance.<n>We introduce a credibility evaluation subset to quantify credibility, uncover a highly significant positive association between shortcut behavior and diagnostic task performance.
• Score: 31.5409689069707
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The potential of Multimodal Large Language Models (MLLMs) in domain of medical imaging raise the demands of systematic and rigorous evaluation frameworks that are aligned with the real-world medical imaging practice. Existing practices that report single or coarse-grained metrics are lack the granularity required for specialized clinical support and fail to assess the reliability of reasoning mechanisms. To address this, we propose a paradigm shift toward multidimensional, fine-grained and in-depth evaluation. Based on a two-stage systematic construction pipeline designed for this paradigm, we instantiate it with MedRCube. We benchmark 33 MLLMs, \textit{Lingshu-32B} achieve top-tier performance. Crucially, MedRCube exposes a series of pronounced insights inaccessible under prior evaluation settings. Furthermore, we introduce a credibility evaluation subset to quantify reasoning credibility, uncover a highly significant positive association between shortcut behavior and diagnostic task performance, raising concerns for clinically trustworthy deployment. The resources of this work can be found at https://github.com/F1mc/MedRCube.

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