Compliance by Design: Governing AI‐Augmented Quantitative Analysis in Clinical and Translational Research
- 1Non-Clinical Statistics, Data Sciences & Analytics, Pfizer Inc., Pearl River, New York, USA.
- 2AI-ML, Quantitative & Digital Sciences, Data Sciences & Analytics, Pfizer Inc., Cambridge, Massachusetts, USA.
- 3Non-Clinical Statistics, Data Sciences & Analytics, Pfizer Inc., Collegeville, Pennsylvania, USA.
Abstract
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.
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BibTeX
@article{zhang2026compliance,
title = {{Compliance by Design: Governing AI‐Augmented Quantitative Analysis in Clinical and Translational Research}},
author = {Hewei Zhang and Sheraz Khan and Charles Y Tan},
journal = {Clinical and translational science},
year = {2026},
month = oct,
doi = {10.1111/cts.70747},
url = {https://europepmc.org/article/PMC/PMC13718455},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Compliance by Design: Governing AI‐Augmented Quantitative Analysis in Clinical and Translational Research
AU - Hewei Zhang
AU - Sheraz Khan
AU - Charles Y Tan
PY - 2026
DA - 2026/10/29
JO - Clinical and translational science
DO - 10.1111/cts.70747
UR - https://europepmc.org/article/PMC/PMC13718455
AB - 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.
SN - 1752-8054
SN - 1752-8062
ER -