Evaluating the Diagnostic Classification Ability of Multimodal Large Language Models: Insights from the Osteoarthritis Initiative

TL;DR

This study assesses the diagnostic classification capabilities of multimodal large language models in medical imaging, revealing that specialized vision encoders outperform full MLLM pipelines in classifying knee osteoarthritis from radiographs.

Contribution

It provides a systematic analysis of MLLM components in medical classification, highlighting the limited role of LLM fine-tuning and emphasizing the importance of vision encoder optimization and data quality.

Findings

Vision encoder alone outperforms full MLLM pipelines in accuracy.

Fine-tuning the LLM offers no significant improvement over prompt guidance.

Balanced, high-quality datasets are more impactful than larger, imbalanced ones.

Abstract

Multimodal large language models (MLLMs) show promising performance on medical visual question answering (VQA) and report generation, but these generation and explanation abilities do not reliably transfer to disease-specific classification. We evaluated MLLM architectures on knee osteoarthritis (OA) radiograph classification, which remains underrepresented in existing medical MLLM benchmarks, even though knee OA affects an estimated 300 to 400 million people worldwide. Through systematic ablation studies manipulating the vision encoder, the connector, and the large language model (LLM) across diverse training strategies, we measured each component's contribution to diagnostic accuracy. In our classification task, a trained vision encoder alone could outperform full MLLM pipelines in classification accuracy and fine-tuning the LLM provided no meaningful improvement over prompt-based guidance. And LoRA fine-tuning on a small, class-balanced dataset (500 images) gave better results than training on a much larger but class-imbalanced set (5,778 images), indicating that data balance and quality can matter more than raw scale for this task. These findings suggest that for domain-specific medical classification, LLMs are more effective as interpreters and report generators rather than as primary classifiers. Therefore, the MLLM architecture appears less suitable for medical image diagnostic classification tasks that demand high certainty. We recommend prioritizing vision encoder optimization and careful dataset curation when developing clinically applicable systems.