Evaluating and Mitigating Factuality Hallucination in Medical Vision Large Language Models
Authors: Peng Xia, Weiran Xia, Wenhao Zheng, Kangyu Zhu, Gang Li, Zongyuan Ge, Huaxiu Yao


Reviewer paper summaries: This paper introduces Rad-RQE, the first benchmark for evaluating factuality hallucination in Medical Large Vision Language Models (Med-LVLMs). Rad-RQE is a diversified and automatically annotated VQA-based benchmark, curated by transforming radiology reports into closed-set question-answer pairs through a multi-round GPT-4 generation and self-checking process. The authors categorize the questions into five medical types to enable fine-grained hallucination analysis.
Using Rad-RQE, the authors evaluate factuality hallucination in three representative Med-LVLMs: LLaVA-Med, MedFlamingo, and XrayGPT. The results reveal that all models exhibit significant factual hallucination to various degrees.
To mitigate hallucination in Med-LVLMs, the authors propose REIN (Retriever for Medical LVLM Hallcuination), a multimodal retriever that uses global and local features to retrieve radiology reports most similar to the target images. The retrieved reports are used to guide the generation process and significantly reduce hallucination.
Summary of strengths: 1. The paper tackles the important and under-explored problem of factuality hallucination in Med-LVLMs, which is critical for their reliable application in medical contexts.
2. The evaluation of three representative Med-LVLMs using Rad-RQE provides valuable insights into the prevalence and characteristics of factuality hallucination in current models.
3. REIN is an effective and efficient method for mitigating hallucination in Med-LVLMs. By using a multimodal retriever with both global and local features, it significantly outperforms existing mitigation methods designed for general vision-language models.
4. The detailed experimental results and analysis, including the fine-grained evaluation across different question types and the ablation study of REIN, strengthen the paper's findings and contributions.
Summary of weaknesses: 1. The overall novelty and contribution of this work is limited, 
2. The manuscript titled with "Mitigating Factuality Hallucination in Medical Large Vision Language Model", however, the model is only evaluated with chest radiology report, which limiting its verification to other subdomains, eg. brain images, spine images, or even pathology, etc. I would suggest limiting the title with more specific scope, or to consider extended evaluation with other datasets.
3. Qualitative analysis is encouraged to provide for detailed models' hallucinatory response.
4. The benchmark of hallucination remains arguable. I don't quite understand how the benchmark is meaningful to evaluate hallucination compared with general errors. I think it would be better if the author can provide examples of the benchmark QA and explain how this can be used for identifying hallucination.
Comments, suggestions and typos: 1. It is encouraged to provide more details on how the benchmark data is manually checked.
2. Is there potential risk for data leakage issue, as XrayGPT is trained on MIMIC and IU Xray data?
3. Missing related literature: https://arxiv.org/abs/2401.05827v2; https://arxiv.org/abs/2406.10185
Overall assessment scores: 2