arXiv cs.AI· Wei-Wei Zhang, Pengkai Wang, Yan Li, Zhijie Sang, Guan-Qun Zhou, Jun-Yan Li, Shu-Han Zhou, Pei-Wen Liu, Ting-Xi Tang, Min Tang, Zhitian Hou, Jianmin Wu, Jian Guan, i Lin, Hongxia Yang, Ying Sun·· 14 小时前AI 评分42
NPC-RubricBench：面向鼻咽癌纵向诊疗全流程的 LLM 分阶段评估基准
Patient-specific rubric-based evaluation of large language models across longitudinal cancer care
AI 导读
研究者提出 NPC-RubricBench，一个以鼻咽癌为疾病场景、覆盖六个连续诊疗阶段的分阶段临床推理基准，基于两个癌症中心 405 名患者的纵向病历构建，含 941 个患者-阶段单元和 17,688 条患者特异性原子评分标准。
正文 · 原文
Authors:Wei-Wei Zhang, Pengkai Wang, Yan Li, Zhijie Sang, Guan-Qun Zhou, Jun-Yan Li, Shu-Han Zhou, Pei-Wen Liu, Ting-Xi Tang, Min Tang, Zhitian Hou, Jianmin Wu, Jian Guan, i Lin, Hongxia Yang, Ying Sun
Abstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.35847 [cs.AI] |
| (or arXiv:2610.35847v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35847 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wang Pengkai [view email]
[v1]
Mon, 26 Oct 2026 17:56:26 UTC (1,287 KB)
来源：arXiv cs.AI · arxiv.org