mlsanityData to 14 Oct 2026

Hallucination Benchmark in Medical Visual Question Answering

arXiv 2401.05827 ↗First posted 11 Jan 2024cs.CL cs.AI cs.CVcited by 18

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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.

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

6 of 7 references.

References · 6

  1. 2023Visual Instruction Tuningcited by 7,869
  2. 2023Improved Baselines with Visual Instruction Tuningcited by 4,418
  3. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,305
  4. 2023PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answeringcited by 338
  5. 2023Med-HALT: Medical Domain Hallucination Test for Large Language Modelscited by 157
  6. 2020Pathological Visual Question Answeringcited by 18

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 10; 2026: 3.

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  1. 2024Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agentscited by 65
  2. 2024MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Modelscited by 53
  3. 2025From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Modelscited by 26
  4. 2024ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agentscited by 20
  5. 2025Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMscited by 14
  6. 2025Vision Language Models in Medicinecited by 12
  7. 2024Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patternscited by 8
  8. 2025Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generationcited by 8
  9. 2025Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigationcited by 4
  10. 2025CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decodingcited by 4
  11. 2024A Unified Hallucination Mitigation Framework for Large Vision-Language Modelscited by 3
  12. 2025DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labelscited by 2
6 more
  1. 2026V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answeringcited by 2
  2. 2025CoTBox-TTT: Grounding Medical VQA with Visual Chain-of-Thought Boxes During Test-time Trainingcited by 1
  3. 2026Unified Hallucination Fuzzing for Multimodal Large Language Modelscited by 1
  4. 2025RadEyeVideo: Enhancing general-domain Large Vision Language Model for chest X-ray analysis with video representations of eye gazecited by 0
  5. 2025Token-Level Inference-Time Alignment for Vision-Language Modelscited by 0
  6. 2026LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answeringcited by 0