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arXiv · 2510.18304

The Impact of Image Resolution on Biomedical Multimodal Large Language Models

Liangyu Chen·James Burgess·Jeffrey J Nirschl·Orr Zohar·Serena Yeung-Levy
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Abstract

Imaging technologies are fundamental to biomedical research and modern medicine, requiring analysis of high-resolution images across various modalities. While multimodal large language models (MLLMs) show promise for biomedical image analysis, most are designed for low-resolution images from general-purpose datasets, risking critical information loss. We investigate how image resolution affects MLLM performance in biomedical applications and demonstrate that: (1) native-resolution training and inference significantly improve performance across multiple tasks, (2) misalignment between training and inference resolutions severely degrades performance, and (3) mixed-resolution training effectively mitigates misalignment and balances computational constraints with performance requirements. Based on these findings, we recommend prioritizing native-resolution inference and mixed-resolution datasets to optimize biomedical MLLMs for transformative impact in scientific research and clinical applications.

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Liangyu Chen, James Burgess, Jeffrey J Nirschl, Orr Zohar, Serena Yeung-Levy. 2025-10-21. The Impact of Image Resolution on Biomedical Multimodal Large Language Models. https://arxiv.org/abs/2510.18304

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