GI-Bench: A Panoramic Benchmark Revealing the Knowledge-Experience Dissociation of Multimodal Large Language Models in Gastrointestinal Endoscopy Against Clinical Standards

• URL: http://arxiv.org/abs/2601.08183v2
• Date: Wed, 14 Jan 2026 02:26:26 GMT
• Title: GI-Bench: A Panoramic Benchmark Revealing the Knowledge-Experience Dissociation of Multimodal Large Language Models in Gastrointestinal Endoscopy Against Clinical Standards
• Authors: Yan Zhu, Te Luo, Pei-Yao Fu, Zhen Zhang, Zi-Long Wang, Yi-Fan Qu, Zi-Han Geng, Jia-Qi Xu, Lu Yao, Li-Yun Ma, Wei Su, Wei-Feng Chen, Quan-Lin Li, Shuo Wang, Ping-Hong Zhou,
• Abstract summary: We evaluate state-of-the-art Multimodal Large Language Models (MLLMs) across a panoramic gastrointestinal endoscopy workflow.<n>We constructed GI-Bench, a benchmark encompassing 20 fine-grained lesion categories.<n>Models generated reports with superior linguistic readability compared with humans.
• Score: 17.453089229230663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models (MLLMs) show promise in gastroenterology, yet their performance against comprehensive clinical workflows and human benchmarks remains unverified. To systematically evaluate state-of-the-art MLLMs across a panoramic gastrointestinal endoscopy workflow and determine their clinical utility compared with human endoscopists. We constructed GI-Bench, a benchmark encompassing 20 fine-grained lesion categories. Twelve MLLMs were evaluated across a five-stage clinical workflow: anatomical localization, lesion identification, diagnosis, findings description, and management. Model performance was benchmarked against three junior endoscopists and three residency trainees using Macro-F1, mean Intersection-over-Union (mIoU), and multi-dimensional Likert scale. Gemini-3-Pro achieved state-of-the-art performance. In diagnostic reasoning, top-tier models (Macro-F1 0.641) outperformed trainees (0.492) and rivaled junior endoscopists (0.727; p>0.05). However, a critical "spatial grounding bottleneck" persisted; human lesion localization (mIoU >0.506) significantly outperformed the best model (0.345; p<0.05). Furthermore, qualitative analysis revealed a "fluency-accuracy paradox": models generated reports with superior linguistic readability compared with humans (p<0.05) but exhibited significantly lower factual correctness (p<0.05) due to "over-interpretation" and hallucination of visual features. GI-Bench maintains a dynamic leaderboard that tracks the evolving performance of MLLMs in clinical endoscopy. The current rankings and benchmark results are available at https://roterdl.github.io/GIBench/.

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