Large language models (LLMs) are increasingly applied to health management, showing promise across disease prevention, clinical decision-making, and long-term care. However, existing medical benchmarks remain largely static and task-isolated, failing to capture the openness, longitudinal structure, and safety-critical complexity of real-world clinical workflows. We introduce ClinConsensus, a Chinese medical benchmark curated, validated, and quality-controlled by clinical experts. ClinConsensus comprises 2500 open-ended cases spanning the full continuum of care--from prevention and intervention to long-term follow-up--covering 36 medical specialties, 12 common clinical task types, and progressively increasing levels of complexity. To enable reliable evaluation of such complex scenarios, we adopt a rubric-based grading protocol and propose the Clinically Applicable Consistency Score (CACS@k). We further introduce a dual-judge evaluation framework, combining a high-capability LLM-as-judge with a distilled, locally deployable judge model trained via supervised fine-tuning, enabling scalable and reproducible evaluation aligned with physician judgment. Using ClinConsensus, we conduct a comprehensive assessment of several leading LLMs and reveal substantial heterogeneity across task themes, care stages, and medical specialties. While top-performing models achieve comparable overall scores, they differ markedly in reasoning, evidence use, and longitudinal follow-up capabilities, and clinically actionable treatment planning remains a key bottleneck. We release ClinConsensus as an extensible benchmark to support the development and evaluation of medical LLMs that are robust, clinically grounded, and ready for real-world deployment.

翻译：大语言模型（LLMs）在健康管理领域的应用日益广泛，在疾病预防、临床决策和长期护理等方面展现出潜力。然而，现有医疗评估基准大多为静态且任务孤立的，未能捕捉真实世界临床工作流程的开放性、纵向结构及安全关键复杂性。我们提出了ClinConsensus，这是一个由临床专家精心策划、验证并进行质量控制的医疗中文评估基准。ClinConsensus包含2500个开放式案例，覆盖从预防、干预到长期随访的完整照护连续过程，涵盖36个医学专科、12种常见临床任务类型，并设置了渐进增加的复杂度层级。为对此类复杂场景进行可靠评估，我们采用基于量规的评分协议，并提出了临床适用一致性分数（CACS@k）。我们进一步引入了双评委评估框架，将高性能的LLM-as-judge与通过监督微调训练的、可本地部署的蒸馏评委模型相结合，实现了与医师判断对齐的可扩展、可复现的评估。利用ClinConsensus，我们对多个主流大语言模型进行了全面评估，揭示了模型在不同任务主题、照护阶段和医学专科间存在显著异质性。尽管表现最佳的模型在总体得分上相近，但其在推理、证据运用和纵向随访能力方面差异明显，且具有临床可操作性的治疗规划仍是关键瓶颈。我们发布ClinConsensus作为一个可扩展的基准，以支持开发并评估那些稳健、临床基础扎实且可投入实际应用的医疗大语言模型。