Why can't Epidemiology be automated (yet)?

TL;DR

This paper explores the potential and limitations of applying AI to automate various tasks in epidemiology, highlighting current capabilities, challenges, and future opportunities for integration.

Contribution

It maps the landscape of epidemiological tasks suitable for AI, identifies current limitations, and demonstrates how agentic AI systems can perform epidemiological analysis.

Findings

AI improves coding and administrative efficiency

Limitations include AI hallucinations and data access barriers

AI can design and execute epidemiological analysis with variable quality

Abstract

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.