Compliance by Design: Governing AI-Augmented Quantitative Analysis in Clinical and Translational Research

Artificial intelligence (AI) is moving from promise to routine practice in clinical and translational research, and large language models (LLMs) are now used to scope analyses, draft and debug code, and accelerate the quantitative work that supports clinical and translational decisions. This adoption is outpacing the operational safeguards that keep such work reproducible and auditable, and the central risk is not only erroneous code but the quieter erosion of the analytical discipline on which trustworthy bench-to-bedside decisions depend. In this position paper, we argue that AI can be integrated into regulated quantitative workflows without sacrificing reproducibility, provided it operates inside a compliance-by-design architecture built on two principles: analytical intent must be formally encoded and locked before execution, and the analysis itself must run outside the LLM as specification-verified code in a validated environment. We describe three failure modes that ordinary quality control does not detect, namely context drift, the displacement effect, and implementation drift, and we link each to documented model behavior. For open-ended investigation, these principles are operationalized as a four-step execution firewall; for recurring development studies such as assay validations and animal safety studies, they are operationalized through a governed library of documented, validated agents, supported by fit-for-purpose validation, a clear separation of human and machine roles, and post-deployment monitoring. Both approaches can be implemented with components available today. Used this way, AI enhances rather than replaces quantitative expertise, which now resides less in the code a model produces than in the constraints a scientist defines before it runs.