Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification

Researchers have found that despite significant advantages in pre-training data, state-of-the-art multimodal large language models (MLLMs) for medical imaging consistently underperform compared to traditional deep learning methods in image classification tasks. This study identifies four failure modes—visual representation quality limitations, fidelity loss in connector projections, reasoning deficits in LLMs, and semantic mapping misalignment—that contribute to the performance degradation of MLLMs, highlighting critical barriers to their clinical deployment.

Read the full article at arXiv cs.CV (Vision)

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