# Hallucination Benchmark in Medical Visual Question Answering

arXiv: https://arxiv.org/abs/2401.05827 · HTML: https://mlsanity.com/paper/2401.05827/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering](https://mlsanity.com/paper/2305.10415/) (2023)
- [Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://mlsanity.com/paper/2307.15343/) (2023)
- [Pathological Visual Question Answering](https://mlsanity.com/paper/2010.12435/) (2020)

## Built on by (most cited first, 18 of 18)

- [Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents](https://mlsanity.com/paper/2411.09523/) (2024)
- [MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models](https://mlsanity.com/paper/2406.07057/) (2024)
- [From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2509.25373/) (2025)
- [ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agents](https://mlsanity.com/paper/2410.17657/) (2024)
- [Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs](https://mlsanity.com/paper/2505.00744/) (2025)
- [Vision Language Models in Medicine](https://mlsanity.com/paper/2503.01863/) (2025)
- [Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns](https://mlsanity.com/paper/2404.02370/) (2024)
- [Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.15040/) (2025)
- [Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigation](https://mlsanity.com/paper/2508.15370/) (2025)
- [CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding](https://mlsanity.com/paper/2509.23379/) (2025)
- [A Unified Hallucination Mitigation Framework for Large Vision-Language Models](https://mlsanity.com/paper/2409.16494/) (2024)
- [DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels](https://mlsanity.com/paper/2503.18536/) (2025)
- [V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answering](https://mlsanity.com/paper/2601.18240/) (2026)
- [CoTBox-TTT: Grounding Medical VQA with Visual Chain-of-Thought Boxes During Test-time Training](https://mlsanity.com/paper/2511.12446/) (2025)
- [Unified Hallucination Fuzzing for Multimodal Large Language Models](https://mlsanity.com/paper/2608.07525/) (2026)
- [RadEyeVideo: Enhancing general-domain Large Vision Language Model for chest X-ray analysis with video representations of eye gaze](https://mlsanity.com/paper/2507.09097/) (2025)
- [Token-Level Inference-Time Alignment for Vision-Language Models](https://mlsanity.com/paper/2510.21794/) (2025)
- [LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering](https://mlsanity.com/paper/2605.09384/) (2026)

## Abstract (arXiv)

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.