医疗AI开发者终于有了评估模型在多疗程动态决策中的基准——ClinicalMC覆盖从分诊到出院的完整流程，做临床决策系统的团队可以直接拿来测模型。
现有基准主要评估大模型在单疗程场景中的表现，缺乏对多疗程（患者病情随时间演变）的系统评估。为此，研究者提出了ClinicalMC基准，包含1275个中文和5804个英文样本，覆盖从入院到出院的四个阶段（分诊、首诊检查/诊断/治疗、后续多疗程检查/评估/治疗、最终诊断）。英文数据集患者平均经历5.11个疗程，中文数据集为3.42个。他们构建了包含患者、考官和医生智能体的多智能体评估框架，并设计了单轮静态和多轮动态两种实验设置，评估了闭源模型（如GPT5-mini）、开源模型（如DeepSeek-V3.2）和医疗模型（如HuatuoGPT-o1）。该工作旨在更好地理解大模型在医疗领域的表现，支持其有效部署。
ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models
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 GPT5-mini; 2) open-source LLMs like DeepSeek-V3.2; 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.