Regulatory-Ready AI in Pharmaceutical Sciences: An Umbrella Review of Explainability, Reproducibility, Bias, Validation, and Governance Readiness

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Artificial intelligence is increasingly applied across drug discovery, pharmaceutical development, clinical pharmacology, pharmaceutics, pharmacovigilance, and related decision-support contexts, yet high predictive performance alone does not establish that an AI system is suitable for regulated or safety-relevant use. This umbrella review synthesized review-level evidence concerning the explainability, reproducibility, bias control, validation, documentation, lifecycle oversight, and governance conditions that may support regulatory-readiness assessment in pharmaceutical sciences. PubMed/MEDLINE, Scopus-indexed and Web of Science-indexed records, ScienceDirect, SpringerLink, Wiley Online Library, Oxford Academic journals, IEEE Xplore, Nature Portfolio journals, Frontiers journals, and MDPI journals were searched for publications dated from 1 January 2017 to 11 July 2026. After database searching, citation chasing, deduplication, title-and-abstract screening, and full-text eligibility assessment, the final evidence corpus contained 37 publications, comprising 31 review-level syntheses and six essential methodological or conceptual anchors. Evidence was extracted by regulatory-readiness domain and synthesized thematically, with methodological limitations appraised narratively and review overlap considered qualitatively. Review-level evidence converged on the need for purpose-specific explainability, transparent data provenance, reproducible workflows, explicit documentation, representative datasets, bias and fairness assessment, leakage-resistant internal validation, independent external validation, prospective evaluation where relevant, uncertainty reporting, human oversight, auditability, lifecycle monitoring, and controlled model updating. However, terminology, evaluation methods, reporting practices, validation depth, and governance implementation were inconsistent. External, prospective, clinical, and long-term lifecycle evidence remained less developed than retrospective performance evidence. Regulatory-ready pharmaceutical AI requires an integrated evidence and governance architecture rather than any single technical feature. Explainability, reproducibility, bias awareness, validation, documentation, oversight, and continuous monitoring may strengthen readiness assessment, but review-level evidence does not itself establish regulatory approval, clinical utility, autonomous decision-making suitability, or deployment readiness.