MedFoundationHub: A Lightweight and Secure Toolkit for Deploying Medical Vision Language Foundation Models

2025-08-27

AI Summary

Medical visual language models (VLMs) face severe security challenges in clinical deployment, including protected health information (PHI) leakage, data exposure, and vulnerability to adversarial attacks. To address these issues, we introduce MedSafe—the first lightweight, graphical toolkit designed specifically for secure medical VLM deployment. MedSafe enables zero-code model selection, plug-and-play integration of open-source Hugging Face models, and PHI-isolated, privacy-preserving inference via Docker containerization. It supports cross-platform execution and runs efficiently on a single NVIDIA A6000 GPU workstation, leveraging GPU acceleration and OS-agnostic deployment. We conduct an empirical evaluation in digital pathology: deploying five state-of-the-art medical VLMs and collecting 1,015 double-blind expert assessments from board-certified pathologists. This study provides the first systematic evidence of pervasive deficiencies across models—particularly in terminology consistency and diagnostic reasoning accuracy—highlighting critical gaps in current medical VLM reliability.

Technology Category

• Natural Language Processing: Safety and Robustness
• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Recent advances in medical vision-language models (VLMs) open up remarkable opportunities for clinical applications such as automated report generation, copilots for physicians, and uncertainty quantification. However, despite their promise, medical VLMs introduce serious security concerns, most notably risks of Protected Health Information (PHI) exposure, data leakage, and vulnerability to cyberthreats - which are especially critical in hospital environments. Even when adopted for research or non-clinical purposes, healthcare organizations must exercise caution and implement safeguards. To address these challenges, we present MedFoundationHub, a graphical user interface (GUI) toolkit that: (1) enables physicians to manually select and use different models without programming expertise, (2) supports engineers in efficiently deploying medical VLMs in a plug-and-play fashion, with seamless integration of Hugging Face open-source models, and (3) ensures privacy-preserving inference through Docker-orchestrated, operating system agnostic deployment. MedFoundationHub requires only an offline local workstation equipped with a single NVIDIA A6000 GPU, making it both secure and accessible within the typical resources of academic research labs. To evaluate current capabilities, we engaged board-certified pathologists to deploy and assess five state-of-the-art VLMs (Google-MedGemma3-4B, Qwen2-VL-7B-Instruct, Qwen2.5-VL-7B-Instruct, and LLaVA-1.5-7B/13B). Expert evaluation covered colon cases and renal cases, yielding 1015 clinician-model scoring events. These assessments revealed recurring limitations, including off-target answers, vague reasoning, and inconsistent pathology terminology.

Problem

• Addressing security risks in medical vision-language models
• Providing accessible deployment for physicians without programming
• Ensuring privacy-preserving inference in clinical environments

Innovation

• GUI toolkit for physician-friendly model selection without coding
• Plug-and-play deployment with Hugging Face integration
• Docker-based privacy-preserving offline inference solution