@inproceedings{hou-etal-2026-clinicalmc,
    title = "{C}linical{MC}: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models",
    author = "Hou, Ruihui  and
      Zhu, Siyi  and
      Huai, Ziyue  and
      Yu, Guangya  and
      Fan, Yongqi  and
      Wang, ChunMing  and
      Ruan, Tong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.943/",
    pages = "18888--18915",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) have been widely adopted in healthcare, yet they still encounter significant challenges in complex clinical decision-making scenarios. Existing benchmarks primarily assess LLM performance in single-course settings and lack systematic evaluation in multi-course scenarios, where a patient{'}s condition evolves over time. To address this gap, we propose ClinicalMC, a benchmark for multi-course clinical decision-making. It includes 1,275 Chinese and 5,804 English samples across four stages from admission to discharge. These stages cover triage, first-course examination/diagnosis/treatment, subsequent multi-course examination/assessment/treatment, and final diagnosis. In ClinicalMC, patients in the English dataset undergo an average of 5.11 clinical courses, whereas those in the Chinese dataset undergo 3.42. To assess LLM performance, we construct a multi-agent evaluation framework that includes patient, examiner, and doctor agents. Based on the benchmark and framework, we design two experimental settings{---}a single-turn static setting and a multi-turn dynamic setting{---}and assess three categories of LLMs: 1) closed-source LLMs like GPT-4o-mini; 2) open-source LLMs like DeepSeek-V3, and 3) medical LLMs like HuatuoGPT-o1. Through extensive evaluation, we aim to better understand LLM performance in the medical domain and support its effective deployment in healthcare."
}