Evaluation of Medical Vision Language Models HuluMed and MedGemma, and general purpose chatbots Gemma 3, ChatGPT Plus, and Claude Pro on real previously unseen wound images
On the map: Multimodal LLMs / vision-language models → Medical vision-language models
Abstract (arXiv)
Chronic wound assessment remains a clinically challenging task that requires accurate interpretation of wound morphology, tissue composition, vascular characteristics, and infection risk. Recent advances in Vision-Language Models (VLMs) have introduced the possibility of automated multimodal wound analysis through image understanding combined with clinical reasoning. This study evaluates the performance of several general-purpose and medically specialized open-source and proprietary VLMs for clinical wound assessment using an expanded, curated dataset of 20 clinically diverse wounds spanning vascular, surgical, ischemic, venous, lymphedema, and amputation-related etiologies. Six VLMs were evaluated using a structured twelve-question clinical framework covering wound classification, infection risk, vascular intervention recommendations, debridement urgency, wound therapy selection, and advanced management planning. Across 20 wound cases and 240 clinician-graded wound-analysis decisions, ChatGPT achieved the highest overall performance with 174/240 correct responses (72.50%), followed by Claude with 149/240 (62.08%). Among the open-source and medically specialized models, HuluMed achieved the strongest performance with 96/240 correct responses (40.00%), followed by Gemma 3 (81/240, 33.75%), MedGemma 4B (62/240, 25.83%), and MedGemma 27B (42/240, 17.50%). The findings suggest that frontier general-purpose multimodal systems currently demonstrate substantially stronger wound-analysis performance than medically specialized alternatives, highlighting the continued importance of broad multimodal reasoning capabilities alongside domain-specific medical knowledge. Although current VLMs demonstrate promising potential for clinical decision support, substantial limitations remain in advanced wound-management reasoning, procedural planning, and autonomous clinical reliability.
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 21 references that are in the map. First posted on arXiv 16 Jun 2026.
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- 2025How Far Have Medical Vision-Language Models Come? A Comprehensive Benchmarking Study15 Jul
- 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day1 Jun
Other references · 10
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
- 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
- 2025Gemma 3 Technical Reportcited by 1,824
- 2020PathVQA: 30000+ Questions for Medical Visual Question Answeringcited by 475
- 2021SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answeringcited by 449
- 2025MedGemma Technical Reportcited by 436
- 2023Med-Flamingo: a Multimodal Medical Few-shot Learnercited by 392
- 2025Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understandingcited by 102
- 2020Fully Automatic Wound Segmentation with Deep Convolutional Neural Networkscited by 32
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- 2025SurgWound-Bench: A Benchmark for Surgical Wound Diagnosis
- 2025MedVision: Benchmarking Quantitative Medical Image Analysis
- 2026How Do Medical MLLMs Fail? A Study on Visual Grounding in Medical Images
- 2026How Medical VLMs Underutilize Their Vision Encoders: A Dermatology Perspective
- 2026Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in Dermatology