ReviewThe AAPS journal2026
Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks.
Review in The AAPS journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital transformation in pharmaceutical regulatory affairs is accelerating as global submissions grow in complexity and traditional document-based workflows reach their limits. Artificial intelligence (AI), particularly natural language processing (NLP), is increasingly being explored to support regulatory data management, document preparation, and decision support activities. This review examines AI adoption across pharmaceutical regulatory science, including initiatives from major regulatory agencies, AI-supported regulatory workflows, and emerging governance and interoperability frameworks. Current applications include document classification, data extraction, Common Technical Document (CTD) support, pharmacovigilance, and predictive analytics. Key implementation challenges, including explainability, traceability, validation, data quality, interoperability, cybersecurity, and Good Practice (GxP) compliance requirements, are critically discussed. The review further examines emerging regulatory data ecosystems and governance frameworks that may support the responsible integration of AI into regulatory processes. Collectively, these developments highlight the potential of AI to support more structured, interoperable, and efficient regulatory systems while maintaining regulatory oversight and accountability. Current evidence suggests that AI implementation has progressed from conceptual research toward early operational deployment. However, robust evidence demonstrating sustained improvements in regulatory performance and long-term operational impact remains limited.
Indexed as
Identifiers
42760430What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.