arXiv cs.AI· Qiaoyu Zheng, Weike Zhao, Yanjie Fan, Ye Cao, Mengqi Jiang, Xiao Zhou, Xiaoman Zhang, Pengcheng Qiu, Yongguo Yu, Yanfeng Wang, Ya Zhang, Chaoyi Wu, Weidi Xie·· 14 小时前AI 评分58
ClinGeX：以技能为中心的智能体推进临床基因组分析
Advancing Clinical Genomic Analysis with a Skill-Centred Agent
AI 导读
研究团队提出临床基因组分析智能体 ClinGeX，通过任务检索与依赖感知编排组合 105 个可执行技能。在 223 个时间留出基准案例上，其任务级一致率为 71.7%，最强对照为 45.7%；45 名临床基因组专业人员的对照实验中，ClinGeX 辅助在变异分析、罕见病基因组解读和分子肿瘤决策上的案例级一致率分别为 51.1%、57.8%、64.4%，并缩短了报告解读时间。
正文 · 原文
Authors:Qiaoyu Zheng, Weike Zhao, Yanjie Fan, Ye Cao, Mengqi Jiang, Xiao Zhou, Xiaoman Zhang, Pengcheng Qiu, Yongguo Yu, Yanfeng Wang, Ya Zhang, Chaoyi Wu, Weidi Xie
Abstract:Clinical genomic analysis requires experts to integrate evolving guidelines, databases, and computational evidence. As sequencing has become increasingly accessible, this complex and labor-intensive process has become a major bottleneck to the timely use of genomic information in clinical decision-making. To fill this gap, we developed ClinGeX, a large-language-model agent that composes 105 curated executable skills through task-specific retrieval and dependency-aware orchestration. On 223 temporally held-out benchmark cases, ClinGeX achieved 71.7\% macro-averaged task-level concordance with expert reference classifications, compared with 45.7\% for the strongest comparator. In a three-condition, counterbalanced human-AI collaboration study, 45 clinical genomic professionals assessed 135 clinically derived cases under routine (no AI assistance), DeepSeek-assisted and ClinGeX-assisted conditions. ClinGeX assistance increased raw case-level concordance across criterion-level variant analysis, genomic interpretation in rare disease and molecular oncology decision-making (51.1\%, 57.8\%, 64.4\% versus 15.6\%, 31.1\%, 20.0\% in comparator conditions) and reduced reported interpretation time in all scenarios. During an exploratory one-month real-world non-interventional assessment, 11 experienced professionals submitted feedback on 96 self-selected analysis episodes and reported frequent conclusion adoption and time savings. These findings support executable domain skills as a promising approach to augment specialist genomic interpretation.
| Subjects: | Artificial Intelligence (cs.AI); Genomics (q-bio.GN) |
| Cite as: | arXiv:2610.40556 [cs.AI] |
| (or arXiv:2610.40556v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40556 arXiv-issued DOI via DataCite |
Submission history
From: Qiaoyu Zheng [view email]
[v1]
Thu, 29 Oct 2026 17:40:24 UTC (7,920 KB)
来源：arXiv cs.AI · arxiv.org