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·· 2 小时前AI 评分56
ClinGeX 论文提出技能中心 Agent 用于临床基因组分析
Advancing Clinical Genomic Analysis with a Skill-Centred Agent
AI 导读
研究团队发布 ClinGeX，一个大语言模型 Agent，通过任务检索和依赖感知编排组合 105 个可执行技能，在 223 个留出基准案例上与专家参考分类达成 71.7% 宏平均任务级一致率，最强对照为 45.7%。
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