PERSPECTIVES观点

Artificial Intelligence–Based Copilots to Generate Causal Evidence

M. Petersen and Others

Abstract

While there is growing consensus that real-world data should play a larger role in generating causal evidence for health care, it is less clear whether and how AI can help. Current approaches to AI-driven analysis of health data are ill-equipped to account for the many threats to causal validity. However, the current human-reliant pipeline for causal analysis also falls short: analyses are complex, require multidisciplinary expertise, and are slow, labor-intensive and error-prone. Here, we speculate how a “human-in-the-loop” AI-based system could help relieve bottlenecks to high-quality causal analyses. We describe how an AI-based causal copilot, leveraging the formal inferential structure of the causal road map, could guide and support researchers through a structured process of translating a causal question into a hypothetical experiment; translating contextual knowledge into transparent and well-justified assumptions; designing, testing, and benchmarking a corresponding statistical analysis plan and code (including integration of machine learning on multimodal data); and supporting causal interpretation of results. Such a system could augment the speed and quality with which researchers conduct causal analyses with real-world data, improve transparency and verification of analyses and assumptions, and ultimately serve as a basis for point-of-care personalized decision support.

DOI: 10.1056/AIp2400727

全文链接：https://ai.nejm.org/doi/abs/10.1056/AIp2400727

基于人工智能的副驾驶生成因果证据

M. Petersen 等人

摘要： 尽管越来越多的共识认为现实世界的数据应该在生成健康护理的因果证据中发挥更大的作用，但人工智能如何帮助这一点尚不清楚。当前的人工智能驱动的健康数据分析方法无法充分考虑到许多对因果有效性的威胁。然而，当前依赖人类的因果分析流程也存在不足：分析复杂，需要多学科专业知识，并且缓慢、劳动密集且容易出错。在这里，我们推测一个“人在循环中”的基于人工智能的系统如何帮助缓解高质量因果分析的瓶颈。我们描述了一个基于人工智能的因果副驾驶，利用因果路线图的形式推理结构，可以指导和支持研究人员通过一个结构化的过程将因果问题转化为假设实验；将上下文知识转化为透明且有充分理由的假设；设计、测试和基准测试相应的统计分析计划和代码（包括多模态数据上的机器学习集成）；并支持结果的因果解释。这样的系统可以增加研究人员使用现实世界数据进行因果分析的速度和质量，提高分析和假设的透明度和可验证性，并最终作为临床个性化决策支持的基础。

NEJM AI, Volume 1 No. 12 December 2024

译文来自于AI工具Kimi