ReviewThe AAPS journal2025
Innovative Approaches in Regulatory Affairs: Leveraging Artificial Intelligence and Machine Learning for Efficient Compliance and Decision-Making.
Review in The AAPS journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed.
- From Compliance to Strategic Partner: The Transformation of Regulatory Affairs in AstraZeneca Local Affiliates.Pharmaceutical medicine · 2026Article
- From Executor to Orchestrator: The Pharmacology Scientist in the Age of Agentic AI.Clinical pharmacology and therapeutics · 2026Review
- Web-based Dossier Management System (DMS) and Emerging Technologies to Facilitate Faster Registration and Access to Human Drugs: NAFDAC's Experience.Therapeutic innovation & regulatory science · 2026Article
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Article
- Beyond the hype: navigating the real-world applications of artificial intelligence (AI) in healthcare.Medical review (2021) · 2026Review
- Multimodal artificial intelligence in glioma management: integrating neuroimaging and hematologic biomarkers for precision oncology.Frontiers in oncology · 2026Review
- AI-assisted crop improvement: new design tools within established regulatory frameworks.Frontiers in plant science · 2026Article
- Recent Developments in Pharmaceutical Spray Drying: Modeling, Process Optimization, and Emerging Trends with Machine Learning.Pharmaceutics · 2025Review
- Unified artificial intelligence framework for modeling pollution dynamics and sustainable remediation in environmental chemistry.Scientific reports · 2025Article
- Improved estimation of intrinsic solubility of drug-like molecules through multi-task graph transformer.Journal of cheminformatics · 2025Article
- Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- In Silico Forensic Toxicology: Is It Feasible?Toxics · 2025Review
- Regulatory Perspectives for AI/ML Implementation in Pharmaceutical GMP Environments.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Regulating the AI-enabled ecosystem for human therapeutics.Communications medicine · 2025Review
- Reimagining drug regulation in the age of AI: a framework for the AI-enabled Ecosystem for Therapeutics.Frontiers in medicine · 2025Article
- Nanomaterials Application for STING Pathway-Based Tumor Immunotherapy.International journal of nanomedicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) and AI-driven technologies are transforming industries across the board, with the pharmaceutical sector emerging as a frontrunner beneficiary. This article explores the growing impact of AI and Machine Learning (ML) within pharmaceutical Regulatory Affairs, particularly in dossier preparation, compilation, documentation, submission, review, and regulatory compliance. By automating time-intensive tasks, these technologies streamline workflows, accelerate result generation, and shorten the product approval timeline. However, despite their immense potential, AI and ML also introduce new challenges. Issues such as AI software validation, data management security and privacy, potential biases, ethical concerns, and change management requirements must be addressed. This review highlights current AI-based tools actively used by regulatory professionals such as DocShifter, Veeva Vault, RiskWatch, Freyr SubmitPro, Litera Microsystems, cortical.io etc., examines both the benefits and obstacles of integrating these advanced systems into regulatory practices. Given the rapid pace of technological innovation, the article underscores the need for proactive collaboration with regulatory bodies to manage these developments. It also stresses the importance of adapting to evolving regulatory frameworks and embracing new technologies. Although regulatory agencies like the United Sates Food and Drug Administration (USFDA), European Medicines Agency (EMA), and Medicines and Healthcare products Regulatory Agency (MHRA) are working on guidelines for AI and ML adoption, clear, standardized protocols are still in the works. While the journey ahead may be complex, the integration of AI promises to fundamentally reshape regulatory processes and accelerate the approval of safe, effective pharmaceutical products.
Indexed as
Identifiers
39776314What 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.