MedFoundationHub: A Lightweight and Secure Toolkit for Deploying Medical Vision Language Foundation Models
On the map: Multimodal LLMs / vision-language models → Medical vision-language models
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 12 of its 25 references that are in the map. First posted on arXiv 28 Aug 2025.
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12 of 25 references.
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- 2025MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning26 Feb
- 2025PolyPath: Adapting a Large Multimodal Model for Multi-slide Pathology Report Generation14 Feb
- 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review4 Mar
Other references · 9
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2023Visual Instruction Tuningcited by 7,869
- 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 538
- 2025MedGemma Technical Reportcited by 436
- 2025HealthBench: Evaluating Large Language Models Towards Improved Human Healthcited by 429
- 2024PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathologycited by 85
- 2023Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generationcited by 37
- 2024Multi-modal Vision Pre-training for Medical Image Analysiscited by 23
- 2025PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialoguecited by 8
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