ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models
The article introduces ClinicalMC, a benchmark designed for evaluating large language models in multi-course clinical decision-making. It addresses the limitations of existing benchmarks that focus on single-course scenarios by providing a comprehensive dataset. The benchmark includes samples in both Chinese and English, covering various stages of patient care from admission to discharge.
- ▪ClinicalMC includes 1,275 Chinese and 5,804 English samples across four stages of clinical care.
- ▪Patients in the English dataset undergo an average of 5.11 clinical courses, while those in the Chinese dataset undergo 3.42.
- ▪The benchmark aims to assess the performance of different categories of large language models in the medical domain.
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Computer Science > Artificial Intelligence arXiv:2606.03157 (cs) [Submitted on 2 Jun 2026] Title:ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models Authors:Ruihui Hou, Siyi Zhu, Ziyue Huai, Guangya Yu, Yongqi Fan, Chunming Wang, Tong Ruan View a PDF of the paper titled ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models, by Ruihui Hou and 6 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have been widely adopted in healthcare, yet they still encounter significant challenges in complex clinical decision-making scenarios.
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