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The Scaffold Effect: How Prompt Framing Skews AI Evaluation in Clinical Vision-Language Models

Artificial Intelligence #artificial intelligence#vision-language models

The Scaffold Effect: How Prompt Framing Skews AI Evaluation in Clinical Vision-Language Models

A study on arXiv evaluating 12 open-weight vision-language models (VLMs) on clinical neuroimaging datasets found that up to 58% of apparent multimodal performance gains are due to prompt framing rather than genuine reasoning. The researchers identified a 'scaffold effect' where merely mentioning MRI availability in the task prompt accounts for 70-80% of F1 improvement, even when no imaging data is present. Expert evaluation also revealed fabrication of neuroimaging-grounded justifications, raising concerns about the reliability of VLM evaluations in clinical settings.

Jun 20, 2026 1 source