MedFM-Robust: Benchmarking Robustness of Medical Foundation Models

Xiangxiang Cui; Tianjin Huang; Yifang Wang; Lijie Hu; Lu Yin

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arXiv · 2605.19027

MedFM-Robust: Benchmarking Robustness of Medical Foundation Models

Xiangxiang Cui·Tianjin Huang·Yifang Wang·Lijie Hu·Lu Yin

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Abstract

Medical foundation models have achieved remarkable clinical performance, yet their robustness under real-world perturbations remains underexplored. We present a robustness benchmark comprising 40 perturbation types (12 base, 28 medical-specific) across eight imaging modalities, evaluating five VLMs (LLaVA-Med, MedGemma, MedGemma-1.5, Gemini-2.5-flash and GPT-4o-mini) on VQA, visual grounding, and captioning, alongside two segmentation models (MedSAM, SAM-Med2D) with five fine-tuning strategies. Our findings reveal: (1) Fine-tuning strategy dominates robustness, with LoRA exhibiting nearly double the degradation of full fine-tuning, while SAM-Med2D's Adapter offers favorable efficiency-robustness trade-off. (2) Medical-specific perturbations disproportionately damage segmentation, with 9 of 15 top corruptions being domain-specific. (3) LoRA-tuned visual grounding drops over 40 points, whereas zero-shot captioning remains stable (<7% drop). Zero-shot VQA shows model-dependent robustness--medical models drop under 20% while Gemini-2.5-flash drops 54%. General-purpose VLMs achieve higher VQA accuracy but fail on grounding; among medical VLMs, MedGemma demonstrates the best overall stability. These results provide deployment guidelines and underscore the necessity of domain-specific robustness evaluation for medical AI. Our code is available at: https://abnerai.github.io/MedFM-Robust.

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Xiangxiang Cui, Tianjin Huang, Yifang Wang, Lijie Hu, Lu Yin. 2026-05-22. MedFM-Robust: Benchmarking Robustness of Medical Foundation Models. https://arxiv.org/abs/2605.19027

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