MedGemma vs GPT-4: Open-Source and Proprietary Zero-shot Medical Disease Classification from Images

The paper compares the open‑source MedGemma-4b-it model, fine‑tuned with LoRA, to the proprietary GPT‑4 on a six‑disease medical image classification task. MedGemma achieved a mean test accuracy of 80.37%, outperforming GPT‑4’s 69.56%, and showed higher sensitivity for critical conditions such as cancer and pneumonia. Detailed confusion matrices and classification reports illustrate the performance differences across all disease categories.

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• llms
• agents
• fine-tuning
• multimodal
• safety