MedGemma vs GPT-4: Open-Source and Proprietary Zero-shot Medical Disease Classification from Images
On the map: Multimodal LLMs / vision-language models → Medical vision-language models
Abstract (arXiv)
Multimodal Large Language Models (LLMs) introduce an emerging paradigm for medical imaging by interpreting scans through the lens of extensive clinical knowledge, offering a transformative approach to disease classification. This study presents a critical comparison between two fundamentally different AI architectures: the specialized open-source agent MedGemma and the proprietary large multimodal model GPT-4 for diagnosing six different diseases. The MedGemma-4b-it model, fine-tuned using Low-Rank Adaptation (LoRA), demonstrated superior diagnostic capability by achieving a mean test accuracy of 80.37% compared to 69.58% for the untuned GPT-4. Furthermore, MedGemma exhibited notably higher sensitivity in high-stakes clinical tasks, such as cancer and pneumonia detection. Quantitative analysis via confusion matrices and classification reports provides comprehensive insights into model performance across all categories. These results emphasize that domain-specific fine-tuning is essential for minimizing hallucinations in clinical implementation, positioning MedGemma as a sophisticated tool for complex, evidence-based medical reasoning.
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 3 of its 8 references that are in the map. First posted on arXiv 29 Dec 2025.
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3 of 8 references.
References · 3
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2022Large Language Models Encode Clinical Knowledgecited by 1,830
- 2021DeepViT: Towards Deeper Vision Transformercited by 289
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