arXiv:2606.24115v1 Announce Type: cross Abstract: Vision-language models (VLMs) are prone to hallucination, which remains a major barrier to their safe deployment in clinical practice. To date, most hallucination detection methods have been evaluated on radiology benchmarks such as MIMIC-CXR and VQA-RAD, while gastrointestinal (GI) endoscopy remains largely underexplored. In this paper, we benchmark nine hallucination detection methods on the Gut-VLM dataset, a GI diagnostic Visual Question Answering (VQA) dataset with 4,392 test VQA pairs, across five VLMs (MedGemma-4B, MedGemma-27B, LLaVA-Me
The proliferation of VLMs in medical fields necessitates robust methods for identifying and mitigating 'hallucinations' to ensure patient safety and build trust in AI diagnostic tools.
This development is crucial for integrating AI safely into high-stakes clinical environments, directly addressing a primary barrier to adoption by improving reliability and accuracy.
The explicit benchmarking of hallucination detection methods for GI endoscopy provides a standardized approach to evaluating VLM trustworthiness in a new critical medical domain.
- · AI safety researchers
- · Healthcare providers
- · VLM developers
- · Patients
- · Untrustworthy VLM models
- · Companies neglecting AI safety standards
Improved reliability and acceptance of VLMs in gastroenterology and other medical specialties.
Increased investment in specialized medical AI models and hallucination detection techniques across the healthcare sector.
Enhanced regulatory scrutiny and potential for new certification standards for AI in clinical practice, driven by robust safety benchmarks.
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Read at arXiv cs.AI