MedGemma vs GPT-4: Open-Source and Proprietary Zero-shot Medical Disease Classification from Images
This addresses the problem of accurate and reliable disease classification from medical images for clinicians, though it is incremental as it compares existing models with fine-tuning.
The study compared the open-source MedGemma model, fine-tuned with LoRA, against the proprietary GPT-4 for zero-shot medical disease classification from images, finding that MedGemma achieved a mean test accuracy of 80.37% versus 69.58% for GPT-4, with higher sensitivity in critical tasks like cancer detection.
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.