ClinGeX 智能体用 105 项技能提升临床基因组分析准确率
Advancing Clinical Genomic Analysis with a Skill-Centred Agent
新加坡团队做了个基因组分析智能体 ClinGeX，105 项可执行技能让专家一致率从 45.7% 拉到 71.7%，做医疗 AI 的可以看看架构。
ClinGeX 是一个面向临床基因组分析的大模型智能体，通过任务检索和依赖编排组合 105 项可执行技能。在 223 个时间隔离基准案例上，其任务级与专家分类一致率达 71.7%，最强对照模型为 45.7%。45 名临床基因组专业人员参与的对照实验中，ClinGeX 辅助下三个场景的一致率分别为 51.1%、57.8%、64.4%，对照条件仅 15.6%、31.1%、20.0%。11 名专业人员为期一个月的实际使用反馈显示结论采纳率高、解读时间缩短。
Advancing Clinical Genomic Analysis with a Skill-Centred Agent
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.