Beyond Symmetric Alignment: Spectral Diagnostics of Modality Imbalance in Vision-Language Models in the Medical Domain
arXiv:2606.04613v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) struggle when applied to medical image-text data, yet the tools available to diagnose this failure remain limited. Existing representation alignment metrics are symmetric, collapsing both modalities into a single score and hiding which modality drives cross-modal degradation. We introduce the Spectral Alignment Score (SAS), an asymmetric metric that projects both modalities onto the principal eigenbasis of an anchor modality and computes eigenvalue-weighted per-eigenmode correlations, resulting in directional score
The proliferation of Vision-Language Models into specialized domains like medicine highlights the urgent need for robust diagnostic tools as their failures become more critical.
This development introduces a critical diagnostic tool, the Spectral Alignment Score, for VLM performance in medical imaging, directly impacting the reliability and safety of AI applications in healthcare.
The ability to asymmetrically identify modality-specific weaknesses in VLMs will lead to more targeted model improvements and more trustworthy AI in medical diagnostics.
- · Medical AI developers
- · Healthcare providers adopting AI
- · Patients receiving AI-assisted care
- · AI research in robust model evaluation
- · Developers of unimodal medical diagnostic tools
- · Companies relying on opaque VLM performance metrics
Improved reliability and adoption of Vision-Language Models in medical contexts.
Faster development cycles for medical AI, as diagnosis of failures becomes more efficient.
Potential for new regulatory frameworks for AI in medicine that incorporate such advanced diagnostic capabilities.
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Read at arXiv cs.LG