Artificial intelligence and target trial emulation: toward scalable and credible real-world evidence

Randomized controlled trials remain the gold standard for causal inference but are often resource-intensive, time-consuming, and operationally complex to conduct. The growing availability of real-world data has created new opportunities for generating real-world evidence (RWE), and target trial emulation (TTE) provides a principled framework for designing observational studies that approximate a target trial. At the same time, artificial intelligence (AI) is increasingly applied in health research, yet its application within TTE has received limited systematic attention.

In this Commentary, we examine how AI may support different components of TTE to enable more scalable and credible RWE generation, while considering the methodological risks such applications may introduce. We highlight potential failure mechanisms that may threaten causal validity and the safeguards needed to preserve methodological validity, transparency, and human oversight. We also outline priorities for empirical evaluation and methodological development to support AI-enhanced RWE generation aligned with clinical and regulatory expectations.

TTE is a way of using routinely collected health-care data to design observational studies that more closely resemble clinical trials. In this Commentary, we discuss how AI could support different components of TTE, from defining study populations to analyzing outcomes. We also highlight important risks, including bias, lack of transparency, and overreliance on automated methods. AI should support, rather than replace, researchers. With appropriate validation, human oversight, and safeguards, it may help make RWE more efficient, reproducible, and trustworthy.