arXiv AI By Federico Felizzi, Francesco Andrea Causio, Bianca Destro Castaniti, Olivia Riccomi, Michele Ferramola, Vittorio De Vita, Alessandro Tosi, Antonio Cristiano, Alessia Longo, Lorenzo De Mori, Chiara Battipaglia, Melissa Sawaya, Luigi De Angelis, Marcello Di Pumpo, Alessandra Piscitelli, Pietro Eric Risuleo, Giulia Vojvodic, Mariapia Vassalli, Nicolo Scarsi, Manuel Del Medico

Conviction Without Evidence: Verdict Stability Is Independent of Visual Grounding in Medical Vision--Language Models

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Vision–language models can correctly diagnose medical questions using only the clinical vignette, without the diagnostic image. In experiments on 60 Italian State Examination items, models defended diagnoses derived from a blank placeholder as firmly as those derived from real images, showing no increased fragility. While different adversarial pressures produced distinct dynamics—one leading to a single decisive flip and the other to continual changes—the instability under contentless doubt reflected item ambiguity rather than lack of evidence.

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arXiv AI
Jul 29

Why Does Grounding Hurt Medical VQA? Benchmarking, Diagnosis, and Fine-Tuning of Vision-Language Models

arXiv:2604. 27720v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly applied to medical visual question answering (Med-VQA), yet whether they can \emph{localize} the evidence behind their answers---a prerequisite for clinical auditability---is poorly characterized.

By Xupeng Chen, Binbin Shi, Chenqian Le, Qifu Yin, Lang Lin, Haowei Ni, Ran Gong, Panfeng Li