MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation
Many rely on synthetic conversations or patient simulators, omit patient-uploaded medical images, or evaluate open-ended clinical responses using multiple-choice or lexical-overlap metrics that poorly reflect clinical quality. We introduce \textbf{MedRealMM}, a large-scale benchmark for multimodal online medical consultation built from de-identified patient-doctor interactions collected from a nationwide Chinese internet hospital. MedRealMM uses a Multimodal Clinical Challenge Point (MCCP) extraction framework to identify clinically demanding moments in authentic consultation trajectories and converts each into a standardized next-response generation task while preserving the preceding text-image context.
- ▪Many rely on synthetic conversations or patient simulators, omit patient-uploaded medical images, or evaluate open-ended clinical responses using multiple-choice or lexical-overlap metrics that poorly reflect clinical quality.
- ▪We introduce \textbf{MedRealMM}, a large-scale benchmark for multimodal online medical consultation built from de-identified patient-doctor interactions collected from a nationwide Chinese internet hospital.
- ▪MedRealMM uses a Multimodal Clinical Challenge Point (MCCP) extraction framework to identify clinically demanding moments in authentic consultation trajectories and converts each into a standardized next-response generation task while prese
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.09142 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
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Computer Science > Artificial Intelligence arXiv:2607.09142 (cs) [Submitted on 10 Jul 2026] Title:MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation Authors:Runhan Shi, Quan Zhou, Yuqian Xu, Shuai Yang, Xin Wu, Zitong Zhou, Hui Liu, Bin Cha, Zheming Wang, Liya Li, Wei Wei, Haoyuan Hu, Jun Xu View a PDF of the paper titled MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation, by Runhan Shi and Quan Zhou and Yuqian Xu and Shuai Yang and Xin Wu and Zitong Zhou and Hui Liu and Bin Cha and Zheming Wang and Liya Li and Wei Wei and Haoyuan Hu and Jun Xu View PDF Abstract:Large language models (LLMs) are increasingly deployed in online medical consultation, yet existing benchmarks remain poorly aligned with real clinical practice.
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