Artificial Intelligence in Pharmaceutical Development: Building Rigorous, Decision-Ready Evidence
Abstract
Artificial intelligence (AI) and machine learning (ML) are reshaping the pharmaceutical lifecycle, from discovery and clinical development through manufacturing and regulatory review to safety surveillance and health economics, creating evidentiary challenges that predictive performance alone cannot address. This review examines the inferential, causal, validation, and documentation requirements for AI-enabled evidence generation in high-stakes settings and argues that they must be built into workflows. We outline the evidentiary functions that AI-derived outputs must satisfy before supporting consequential decisions: estimand definition, fit-for-purpose data collection, bias control, uncertainty quantification, validation, interpretability, and decision-grade documentation. Because AI both depends on and accelerates large-scale, non-randomized data collection, the integration of randomized controlled trials (RCTs) with real-world data (RWD) is where its evidentiary risks concentrate; we therefore present a five-step causal roadmap for that integration, in which external data supplement but never replace a concurrent control, and characterize the safeguards required across development, pharmacovigilance, regulation, and health technology assessment. Priority frontiers answer recurring implementation problems: conformal prediction for overconfident predictions, randomization-based inference for scarce data, and federated causal inference and privacy-preserving generative modeling for data that cannot be pooled. Evidence generation depends not on competition between RCTs and RWD, or between statistics and AI/ML, but on their principled synthesis under causal and inferential frameworks, with statistical expertise central to decision-ready evidence.
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BibTeX
@misc{luo2026artificial,
title = {{Artificial Intelligence in Pharmaceutical Development: Building Rigorous, Decision-Ready Evidence}},
author = {Xin Luo and Margaret Gamalo and Achilleas Livieratos and Maria Kudela and Qilu Yu and Yunxun Wang and Junjing Lin and Yuxi Zhao and Allshine Chen},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00557},
eprint = {2611.00557},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00557},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Artificial Intelligence in Pharmaceutical Development: Building Rigorous, Decision-Ready Evidence
AU - Xin Luo
AU - Margaret Gamalo
AU - Achilleas Livieratos
AU - Maria Kudela
AU - Qilu Yu
AU - Yunxun Wang
AU - Junjing Lin
AU - Yuxi Zhao
AU - Allshine Chen
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00557
UR - https://arxiv.org/abs/2611.00557
AB - Artificial intelligence (AI) and machine learning (ML) are reshaping the pharmaceutical lifecycle, from discovery and clinical development through manufacturing and regulatory review to safety surveillance and health economics, creating evidentiary challenges that predictive performance alone cannot address. This review examines the inferential, causal, validation, and documentation requirements for AI-enabled evidence generation in high-stakes settings and argues that they must be built into workflows. We outline the evidentiary functions that AI-derived outputs must satisfy before supporting consequential decisions: estimand definition, fit-for-purpose data collection, bias control, uncertainty quantification, validation, interpretability, and decision-grade documentation. Because AI both depends on and accelerates large-scale, non-randomized data collection, the integration of randomized controlled trials (RCTs) with real-world data (RWD) is where its evidentiary risks concentrate; we therefore present a five-step causal roadmap for that integration, in which external data supplement but never replace a concurrent control, and characterize the safeguards required across development, pharmacovigilance, regulation, and health technology assessment. Priority frontiers answer recurring implementation problems: conformal prediction for overconfident predictions, randomization-based inference for scarce data, and federated causal inference and privacy-preserving generative modeling for data that cannot be pooled. Evidence generation depends not on competition between RCTs and RWD, or between statistics and AI/ML, but on their principled synthesis under causal and inferential frameworks, with statistical expertise central to decision-ready evidence.
ER -