[2607.26886] Hearsay: Vision-Language Medical Diagnoses Without an Image

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arXiv:2607.26886 (cs)

[Submitted on 29 Jul 2026]

Title:Hearsay: Vision-Language Medical Diagnoses Without an Image

Authors:Siddharth Vohra<br>View a PDF of the paper titled Hearsay: Vision-Language Medical Diagnoses Without an Image, by Siddharth Vohra

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Abstract:When asked to describe a medical image that was never attached, frontier vision-language models do not abstain: they confabulate a diagnosis. We show that this confabulation is not random. It is structured by who the patient is said to be. Across chest X-ray, brain MRI, and dermatology, Claude Opus-4.7, GPT-5.4, and Gemini-3.1-Pro are each queried with only a demographic descriptor and no image, and changing the descriptor systematically shifts the diagnosis returned. Claude concentrates sharply: a 65-year-old white man asking about a skin mole receives Melanoma in nearly every response, and a 32-year-old Black woman asking about her chest X-ray receives a Sarcoidosis diagnosis whose reasoning reads "suspected, based on demographics and classic pattern.'' GPT-5.4's effect is broader, fabricating across every demographic cell we test, most conspicuously naming Sarcoidosis for young Black patients on chest X-ray. Two structural findings sharpen the problem. A hedged regime appears in which the prose acknowledges the missing image while the structured diagnosis field nevertheless names a disease, a dissociation invisible to prose-only audits. And Claude's dermatology effect collapses entirely when 'skin mole' is swapped for 'skin lesion' while GPT-5.4's is preserved, indicating that mirage is a family of distinct failure modes rather than a single phenomenon. Trustworthy VLM deployment in clinical pipelines requires auditing the structured output channel directly, and probe-word sensitivity should be treated as a first-class evaluation dimension

Comments:<br>Peer-reviewed and presented at the 1st Workshop on Toward Trustworthy Vision-Language Models in the Wild (TrustVLM), co-located with ACM ICMR 2026, Amsterdam. Non-archival workshop. Reviews public on OpenReview. 5 pages, 2 figures

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)

ACM classes:<br>I.2.7; I.2.10; J.3; K.4.1

Cite as:<br>arXiv:2607.26886 [cs.CV]

(or<br>arXiv:2607.26886v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.26886

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Siddharth Vohra [view email]<br>[v1]<br>Wed, 29 Jul 2026 13:15:23 UTC (271 KB)

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