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Frontier VLMs fail medical VQA tests due to poor grounding and confusion

By PulseAugur Editorial · [3 sources] ·

A new paper evaluates five leading vision-language models (VLMs) on their trustworthiness for medical visual question answering (VQA). The study found significant limitations in the models' ability to accurately localize anatomical targets and a tendency for laterality confusion, with the best model achieving only 0.23 mean IoU. Integrating localization into a pipeline further degraded performance, highlighting grounding as a key bottleneck. While domain adaptation shows promise for improving VQA accuracy, the perception and trustworthiness issues remain. AI

IMPACT Identifies critical perception and grounding failures in frontier VLMs for medical applications, suggesting domain adaptation is needed to improve trustworthiness.

RANK_REASON Academic paper evaluating frontier models on a specific task.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Frontier VLMs fail medical VQA tests due to poor grounding and confusion

COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Xupeng Chen, Binbin Shi, Chenqian Le, Qifu Yin, Lang Lin, Haowei Ni, Ran Gong, Panfeng Li ·

    Auditing Frontier Vision-Language Models for Trustworthy Medical VQA: Grounding Failures, Format Collapse, and Domain Adaptation

    arXiv:2604.27720v1 Announce Type: new Abstract: Deploying vision-language models (VLMs) in clinical settings demands auditable behavior under realistic failure conditions, yet the failure landscape of frontier VLMs on specialized medical inputs is poorly characterized. We audit f…

  2. arXiv cs.AI TIER_1 English(EN) · Panfeng Li ·

    Auditing Frontier Vision-Language Models for Trustworthy Medical VQA: Grounding Failures, Format Collapse, and Domain Adaptation

    Deploying vision-language models (VLMs) in clinical settings demands auditable behavior under realistic failure conditions, yet the failure landscape of frontier VLMs on specialized medical inputs is poorly characterized. We audit five recent frontier and grounding-aware VLMs (Ge…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Auditing Frontier Vision-Language Models for Trustworthy Medical VQA: Grounding Failures, Format Collapse, and Domain Adaptation

    Deploying vision-language models (VLMs) in clinical settings demands auditable behavior under realistic failure conditions, yet the failure landscape of frontier VLMs on specialized medical inputs is poorly characterized. We audit five recent frontier and grounding-aware VLMs (Ge…

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