ACL Findings2025
Can We Trust AI Doctors? A Survey of Medical Hallucination in Large Language and Large Vision-Language Models
Zhihong Zhu, Yunyan Zhang, Xianwei Zhuang, Fan Zhang, Zhongwei Wan, Yuyan Chen, Qingqing Long, Yefeng Zheng, Xian Wu
Abstract
Hallucination has emerged as a critical challenge for large language models (LLMs) and large vision-language models (LVLMs), particularly in high-stakes medical applications. Despite its significance, dedicated research on medical hallucination remains unexplored. In this survey, we first provide a unified perspective on medical hallucination for both LLMs and LVLMs, and delve into its causes. Subsequently, we review recent advancements in detecting, evaluating, and mitigating medical hallucinations, offering a comprehensive overview of evaluation benchmarks, metrics, and strategies developed to tackle this issue. Moreover, we delineate the current challenges and delve into new frontiers, thereby shedding light on future research. We hope this work can provide the community with quick access and spur breakthrough research in this area.
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Cited by top-tier papers4
- MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language ModelsFan Zhang, Zebang Cheng, Chong Deng, Haoxuan Li et al.ICLR 2026 · 23 citations
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- MEDA: Medical-Oriented Activation Editing for Hallucination Mitigation in Medical Large Vision-Language ModelTianbo Wang, Yuqing Ma, Lingyan Meng, Zhange Zhang et al.ICML 2026
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- Visual Instruction TuningHaotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae LeeNeurIPS 2023 · 11,349 citations
- Supervised Contrastive LearningPrannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna et al.NeurIPS 2020 · 7,049 citations
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