NPC-RubricBench 发布：评估 LLM 在癌症纵向诊疗全程中的表现
Patient-specific rubric-based evaluation of large language models across longitudinal cancer care
用405个鼻咽癌患者病历做了个测AI全流程看病的新基准，30个模型里诊断强的到治疗方案就掉链子，做医疗AI的值得看看细节。
arXiv 论文提出 NPC-RubricBench，以鼻咽癌为病种、覆盖 6 个诊疗阶段的纵向临床推理基准。数据来自两家癌症中心 405 名患者的纵向病历，包含 941 个患者-阶段单元和 17,688 条患者专属评分标准。团队评测了 30 个通用与医疗 LLM，发现诊断成绩高不等于后续治疗决策好。解剖分期阶段以显式决策错误为主（48.2%），初始检查和治疗后随访阶段则以决策遗漏为主（33.4% 和 30.4%），医疗特化模型的遗漏率高于通用模型。实验还显示开放式提问降低 Decision Quality，而清单式提示虽提升决策质量却削弱 Evidence Faithfulness 与 Safety Adherence。
Patient-specific rubric-based evaluation of large language models across longitudinal cancer care
Large language models (LLMs) show promise in specialty medicine, yet existing evaluations largely focus on isolated clinical encounters rather than tracking how clinical responsibilities evolve across a longitudinal care pathway. Here we introduce NPC-RubricBench, a phase-aware benchmark for evaluating multistage clinical reasoning across the cancer care continuum, using nasopharyngeal carcinoma as a defined disease context. Curated from longitudinal records of 405 patients across two cancer centers, the benchmark comprises 941 patient-phase units across six sequential care phases and 17,688 patient-specific atomic criteria spanning five evaluation dimensions, with each assessment strictly restricted to decision-time clinical evidence. Evaluating 30 general-purpose and medical LLMs revealed that strong diagnostic performance does not carry over to downstream care. We evaluated 30 general-purpose and medical LLMs, showing that high diagnostic performance did not translate into improved downstream care. Instead, the dominant failure modes reversed across phases. Explicit decision errors dominated anatomical staging at 48.2%, whereas decision omissions dominated initial work-up and post-treatment follow-up at 33.4% and 30.4%, respectively. In the evaluated model set, medically adapted models showed higher omission rates than general-purpose models. Moreover, varying query granularity exposes a critical specification trade-off: broad open-ended requests degrade Decision Quality across all 30 models, while more detailed, checklist-like prompts raise Decision Quality but reduce Evidence Faithfulness and Safety Adherence. Furthermore, NPC-RubricBench demonstrates that static diagnostic benchmarks are insufficient to ensure consistent clinical reliability over time, highlighting the importance of phase-adaptive safeguards in multistage medical AI.