MedFM-Robust: Benchmarking Robustness of Medical Foundation Models
MedFM-Robust is a new benchmark for evaluating the robustness of medical foundation models under real-world conditions, covering medical vision-language models and segmentation foundation models.
[2605.19027] MedFM-Robust: Benchmarking Robustness of Medical Foundation Models
[Submitted on 18 May 2026]
Title:MedFM-Robust: Benchmarking Robustness of Medical Foundation Models
View a PDF of the paper titled MedFM-Robust: Benchmarking Robustness of Medical Foundation Models, by Xiangxiang Cui and 4 other authors
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Abstract:Medical foundation models (MedFMs) have emerged as transformative tools in healthcare, demonstrating capabilities across diverse clinical applications. These models can be broadly categorized into two paradigms: Medical Vision-Language Models (Med-VLMs) and segmentation foundation models. Med-VLMs range from medical-specialized models such as LLaVA-Med and MedGemma, to general-purpose models like GPT-4o and Gemini, all capable of medical image understanding tasks including visual question answering (VQA), report generation, and visual grounding. Concurrently, the Segment Anything Model (SAM) has catalyzed a new generation of medical segmentation models, with adaptations like SAM-Med2D and MedSAM. The widespread clinical deployment of these models thus necessitates rigorous evaluation of their reliability under real-world conditions.
Comments: MICCAI2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.19027 [cs.CV]
(or arXiv:2605.19027v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.19027
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yifang Wang [view email] [v1] Mon, 18 May 2026 18:50:56 UTC (5,625 KB)
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