Modelpedia
Work in progress
About
Findings
Models
Concepts
Methods
Datasets
Sources
Light
Dark
MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models
2025-01-22
· ICLR 2025 Poster ·
anchor
Findings
IC-155
All 13 evaluated MLLMs perform at or near random guessing on MediConfusion, with confusion scores often exceeding 90%, indicating they cannot distinguish visually dissimilar radiology image pairs
IC-156
Gemini models show substantially lower confusion scores than other MLLMs yet still perform at or near random guessing, suggesting their bottleneck is medical knowledge or reasoning rather than visual encoding
IC-157
GPT-4o's MediConfusion performance is robust to prompt format while InstructBLIP is highly sensitive and LLaVA-Med fails completely on multiple-choice evaluation
IC-158
Fine-tuning LLaVA-Med on MediConfusion training pairs cannot achieve 100% training accuracy, indicating the vision encoder's embeddings are fundamentally ambiguous for the confusing pairs