发表机构
Department of Medical Research, China Medical University Hospital; Master Program for Digital Health Innovation, China Medical University; Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences; AI-Driven Genomic Medicine and Drug Discovery Lab, China Medical University Hospital（中国医药大学附设医院医学研究部; 中国医药大学数字健康创新硕士项目; 理化学研究所综合医学科学中心统计与转化遗传学实验室; 中国医药大学附设医院人工智能驱动的基因组医学与药物发现实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究以高血压GWAS为例，介绍受治理的端到端智能研究系统NAIS，其集成多种功能。通过真实数据验证，NAIS能支持生物医学发现，规划队列提取等，重现高血压基因座，在药物性肝损伤预测中也有成果，可产生与专家主导流程相当的输出。
AI 中文摘要
智能研究系统正在成为协调科学工作流程的新范式。在机构生物医学环境中部署需要研究规划、数据访问、工作流编排、证据跟踪、可重复性和人工监督等治理机制。我们提出了NVAITC人工智能科学家（NAIS），这是一个受治理的端到端智能研究系统，旨在支持通用领域科学工作流程，同时将受保护数据保持在机构隐私范围内。NAIS集成了提案审查、执行规划、受治理的计算路由、可重复的工作流编排、证据生成和人工监督。我们在一项真实世界的高血压全基因组关联研究（GWAS）中验证了NAIS，使用了来自286422个人的与医院相关的基因型和电子健康记录（EHR）数据，遵循仅汇总数据政策。该智能体规划了队列提取，编排了GWAS执行，生成了质量控制摘要，并起草了面向出版物的输出。人工-人工智能审查识别了表型差异，并实现了高血压定义的迭代完善。协调后，智能体编排的GWAS重现了已确定的高血压基因座，包括FGF5、ATP2B1、CNNM2、FTO和GRB14，FGF5处最强信号达到$-\log_{10}(p) \sim 70$。作为第二个演示，NAIS还支持药物性肝损伤预测工作流程，多模态图神经网络AUC达到0.842。这些结果表明，受治理的智能研究系统可以支持可扩展的人工智能辅助生物医学发现，同时产生与专家主导的工作流程相当的输出。
英文摘要
Agentic research systems are emerging as a new paradigm for coordinating scientific workflows beyond isolated model inference, code generation, or statistical analysis. However, deployment in institutional biomedical environments requires governed mechanisms for research planning, data access, workflow orchestration, evidence tracking, reproducibility, and human oversight. We present NVAITC AI Scientist (NAIS), a governed end-to-end agentic research system designed to support domain-general scientific workflows while keeping protected data within institutional privacy boundaries. NAIS integrates proposal review, execution planning, governed computational routing, reproducible workflow orchestration, evidence generation, and scientist-in-the-loop oversight. We validate NAIS in a real-world hypertension genome-wide association study (GWAS) using hospital-linked genotype and electronic health record (EHR) data from 286,422 individuals under an aggregate-only data policy. The agent planned cohort extraction, orchestrated GWAS execution, generated quality-control summaries, and drafted publication-oriented outputs. Human-AI review identified phenotype discrepancies and enabled iterative refinement of the hypertension definition. After reconciliation, the agent-orchestrated GWAS reproduced established hypertension loci, including FGF5, ATP2B1, CNNM2, FTO, and GRB14, with the strongest signal at FGF5 reaching $-\log_{10}(p) \sim 70$. As a secondary demonstration, NAIS also supported a drug-induced liver injury prediction workflow, achieving a multimodal graph neural network AUC of 0.842. These results demonstrate that governed agentic research systems can support scalable AI-assisted biomedical discovery while producing outputs comparable to expert-led workflows.
Comments22 pages, 6 figures, 4 tables
