A new perturbation test shows that medical vision-language models rely more on clinical text than on images, with calibration errors growing when text conflicts with the image.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
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On the Risk of Misleading Reports: Diagnosing Textual Biases in Multimodal Clinical AI
A new perturbation test shows that medical vision-language models rely more on clinical text than on images, with calibration errors growing when text conflicts with the image.