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October 20, 2025Open Access

MedHEval: Benchmarking Hallucinations and Mitigation Strategies in Medical Large Vision-Language Models

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Authors

ACAofei ChangPennsylvania State UniversityLHLongjun HuangZhejiang Chinese Medical UniversityPBParminder BhatiaGeneral Electric (Spain)

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Implication

MedHEval benchmark assesses hallucinations in medical LVLMs using various evaluation metrics, indicating current mitigation strategies are inadequate.

Key Points

  • Med-LVLMs struggle with hallucinations from visual misinterpretation and knowledge deficiency, highlighting a significant flaw.
  • Experiments across 11 Med-LVLMs reveal that current mitigation techniques are largely ineffective for context-based errors.
  • Enhanced alignment training is crucial, as existing methods show limited effectiveness in addressing hallucination sources.
  • MedHEval establishes a comprehensive framework for evaluating and mitigating hallucinations in medical models.

Cite This Study

Chang et al. (2025) studied this question.

synapsesocial.com/papers/68f64fbb2509bc8625bfb126https://doi.org/10.48550/arxiv.2503.02157
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Detecting and Evaluating Medical Hallucinations in Large Vision Language Models2024 · 7 citations
  2. 2MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context2024 · 5 citations
  3. 3Med-VCD: Mitigating hallucination for medical large vision language models through visual contrastive decoding2025 · 6 citations
  4. 4Hallucination Detection in Biomedical LLMs2025
  5. 5Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models2024 · 2 citations