What question did this study set out to answer?

The aim is to explore the integration of AI in causal inference for drug development within regulatory frameworks.

March 1, 2026Open Access

Methodological and regulatory considerations for causal AI in drug development

Key Points

The aim is to explore the integration of AI in causal inference for drug development within regulatory frameworks.
Review of current regulatory guidance on AI adoption in drug development
Examination of statistical methodologies relevant to AI-driven causal inference
Discussion on the application of AI across various data sources
Identified gaps in regulatory guidance regarding AI use for causal inference
Highlighted potential benefits of AI for understanding treatment effects
Outlined key regulatory challenges faced by agencies in adopting AI methodologies.

Abstract

Abstract Advances in AI offer significant opportunities to enhance drug development. While several regulatory agencies have begun issuing guidance on AI adoption, its application to causal inference—a critical piece to understand treatment effects and inform regulatory decisions—remains limited. This paper reviews regulatory activities and examines statistical methodologies for AI-driven causal inference. We discuss key regulatory challenges and illustrate how AI adds value across diverse data sources and studies.

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Cite This Study

Lee et al. (Fri,) studied this question.

synapsesocial.com/papers/69a3d824ec16d51705d2eb88 https://doi.org/https://doi.org/10.1038/s41746-026-02477-w

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