Challenges of using artificial intelligence (Natural Language Processing) tools for regulatory documentation

Artificial intelligence (AI), specifically Natural Language Processing (NLP), is revolutionizing pharmaceutical regulatory documents by facilitating automated extraction of data, real-time regulatory change monitoring, and standardization of submissions like the Common Technical Document (CTD), enhancing accuracy and efficiency over the conventional manual approach. In spite of these advantages, obstacles exist in dealing with dynamic regulatory needs, intricate legal and scientific terminology, algorithmic uncertainty, and issues relating to data quality, ethics, and compliance, particularly when NLP technologies become more integrated into Software as a Medical Device. For an investigation of these concerns, this narrative review reviewed regulatory materials from authorities such as the U.S. Food and Drug Administration, European Medicines Agency, Medicines and Healthcare Products Regulatory Agency, and international organizations such as the World Health Organization and co-operation and development, complemented by a subject-specific search of Scopus, PubMed, and Google Scholar (2015–2025). English-language peer-reviewed articles, regulatory guidelines, and industry reports relevant to the topic were included and thematically synthesized under opportunities, challenges, ethical issues, and regulatory views. The results indicate that despite the immense potential of AI/NLP solutions to improve regulatory effectiveness and transparency, they pose intricate technical, regulatory, ethical, and operational issues requiring harmonized international standards, stringent validation frameworks, and multistakeholder engagement to deploy safely and effectively in regulatory science.