# Hallucination Benchmark in Medical Visual Question Answering

Source: [Hallucination Benchmark in Medical Visual Question Answering](<https://www.sota2.com/research/paper/68fa72ba1e209131cea58321>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:32:50.348Z

Authors: Jinge Wu, Yunsoo Kim, Honghan Wu

Published: 2024-01-11T10:52:17.000Z

## Abstract

The recent success of large language and vision models (LLVMs) on vision question answering (VQA), particularly their applications in medicine (Med-VQA), has shown a great potential of realizing effective visual assistants for healthcare. However, these models are not extensively tested on the hallucination phenomenon in clinical settings. Here, we created a hallucination benchmark of medical images paired with question-answer sets and conducted a comprehensive evaluation of the state-of-the-art models. The study provides an in-depth analysis of current models' limitations and reveals the effectiveness of various prompting strategies.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.05827>)

[PDF](<https://arxiv.org/pdf/2401.05827.pdf>)