标题： 为什么流行病学还不能被自动化（目前）？ 显示英文标题

标题： Why can't Epidemiology be automated (yet)?

摘要： 近年来，人工智能（AI）的发展，特别是生成式AI，为加速甚至自动化流行病学研究带来了新的机遇。与基于物理实验的学科不同，流行病学的相当一部分依赖于二次数据分析，因此非常适合这种增强。然而，目前尚不清楚哪些具体任务可以从AI干预中受益，或者存在哪些障碍。对当前AI能力的认识也存在分歧。在此，我们利用现有数据集绘制流行病学任务的图景——从文献综述到数据获取、分析、撰写和传播——并确定现有的AI工具在哪些地方能提高效率。虽然AI可以在某些领域（如编码和行政任务）提高生产力，但其效用受到现有AI模型（例如文献综述中的幻觉）和人类系统（例如访问数据集的障碍）的限制。通过展示AI生成的流行病学成果的例子，包括完全由AI生成的论文，我们证明了最近开发的代理系统现在可以设计和执行流行病学分析，尽管质量参差不齐（见https://github.com/edlowther/automated-epidemiology）。流行病学家现在有机会对AI系统进行实证测试和基准测试；实现AI的潜力需要流行病学家和工程师之间的双向互动。 显示英文摘要

摘要： Recent advances in artificial intelligence (AI) - particularly generative AI - present new opportunities to accelerate, or even automate, epidemiological research. Unlike disciplines based on physical experimentation, a sizable fraction of Epidemiology relies on secondary data analysis and thus is well-suited for such augmentation. Yet, it remains unclear which specific tasks can benefit from AI interventions or where roadblocks exist. Awareness of current AI capabilities is also mixed. Here, we map the landscape of epidemiological tasks using existing datasets - from literature review to data access, analysis, writing up, and dissemination - and identify where existing AI tools offer efficiency gains. While AI can increase productivity in some areas such as coding and administrative tasks, its utility is constrained by limitations of existing AI models (e.g. hallucinations in literature reviews) and human systems (e.g. barriers to accessing datasets). Through examples of AI-generated epidemiological outputs, including fully AI-generated papers, we demonstrate that recently developed agentic systems can now design and execute epidemiological analysis, albeit to varied quality (see https://github.com/edlowther/automated-epidemiology). Epidemiologists have new opportunities to empirically test and benchmark AI systems; realising the potential of AI will require two-way engagement between epidemiologists and engineers.