Measuring What VLMs Don't Say: Validation Metrics Hide Clinical Terminology Erasure in Radiology Report Generation

• URL: http://arxiv.org/abs/2603.01625v1
• Date: Mon, 02 Mar 2026 08:59:39 GMT
• Title: Measuring What VLMs Don't Say: Validation Metrics Hide Clinical Terminology Erasure in Radiology Report Generation
• Authors: Aditya Parikh, Aasa Feragen, Sneha Das, Stella Frank,
• Abstract summary: This paper investigates the use of decoding strategies that lead to high aggregate token-overlap scores despite template collapse.<n>We introduce Clinical Association Displacement (CAD), a vocabulary-level framework that quantifies shifts in demographic-based word associations in generated reports.<n>We show that deterministic decoding produces high levels of semantic erasure, while sampling generates diverse outputs but risks introducing new bias.
• Score: 10.15221228043609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable deployment of Vision-Language Models (VLMs) in radiology requires validation metrics that go beyond surface-level text similarity to ensure clinical fidelity and demographic fairness. This paper investigates a critical blind spot in current model evaluation: the use of decoding strategies that lead to high aggregate token-overlap scores despite succumbing to template collapse, in which models generate only repetitive, safe generic text and omit clinical terminology. Unaddressed, this blind spot can lead to metric gaming, where models that perform well on benchmarks prove clinically uninformative. Instead, we advocate for lexical diversity measures to check model generations for clinical specificity. We introduce Clinical Association Displacement (CAD), a vocabulary-level framework that quantifies shifts in demographic-based word associations in generated reports. Weighted Association Erasure (WAE) aggregates these shifts to measure the clinical signal loss across demographic groups. We show that deterministic decoding produces high levels of semantic erasure, while stochastic sampling generates diverse outputs but risks introducing new bias, motivating a fundamental rethink of how "optimal" reporting is defined.

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