July 2, 2024Open Access

MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context

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Medical large vision language models show increased susceptibility to hallucination compared to general models, despite demonstrating high performance on standard medical tasks.
Assessment using the Medical Visual Hallucination Test across five tasks evaluates domain-specific fine-tuning, testing visual input comprehension and long textual response generation.
Findings emphasize that domain-specific large vision language models require robust reasoning alongside medical knowledge to prevent hallucination before real-world clinical deployment.

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Implication

Benchmark analysis reveals elevated hallucination susceptibility in medical vision language models compared to general models, highlighting reliability risks in clinical settings.

Cite This Study

Gu et al. (2024) studied this question.

synapsesocial.com/papers/68e61a64b6db6435875acf52 https://doi.org/10.48550/arxiv.2407.02730

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Implication

Benchmark analysis reveals elevated hallucination susceptibility in medical vision language models compared to general models, highlighting reliability risks in clinical settings.

MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context

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Discussion

Implication

Cite This Study

MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context

Key Points

Discussion

Implication

Cite This Study

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