Medical-Checklist: Assessing the Comprehension of Medical Images by Multimodal Models
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TL;DR - Medical-Checklist is a binary caption-selection benchmark testing whether multimodal models genuinely comprehend medical images. Its evaluation suggests strong performance on tasks such as Med-VQA may not reflect reliable visual understanding needed for clinical use.
- Each example pairs an image with correct and incorrect captions differing by one substituted medical concept.
- The simple format supports unified evaluation across models built and trained using different approaches.
- The benchmark covers multiple medical subdomains, reduces potential data biases, and tests out-of-distribution inputs.
- Four state-of-the-art medical multimodal models showed image-comprehension weaknesses despite strong task-specific performance.
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Medical-Checklist: Assessing the Comprehension of Medical Images by Multimodal Models
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TL;DR - Medical-Checklist is a binary caption-selection benchmark testing whether multimodal models genuinely comprehend medical images. Its evaluation suggests strong performance on tasks such as Med-VQA may not reflect reliable visual understanding needed for clinical use.
- Each example pairs an image with correct and incorrect captions differing by one substituted medical concept.
- The simple format supports unified evaluation across models built and trained using different approaches.
- The benchmark covers multiple medical subdomains, reduces potential data biases, and tests out-of-distribution inputs.
- Four state-of-the-art medical multimodal models showed image-comprehension weaknesses despite strong task-specific performance.