AI Language Models in Pharmaceutical R&D: Regulatory Compliance and Ethical Considerations

Abstract

The integration of AI language models such as GPT, BERT, and other transformer-based systems is transforming pharmaceutical R&D by enhancing drug discovery, clinical trial design, regulatory documentation, and pharmacovigilance through automation of literature mining, protocol drafting, and safety reporting. Their ability to analyze vast unstructured data accelerates decision-making and shortens time-to-market for new therapies. However, adoption in regulated pharma environments faces challenges including lack of harmonized validation guidelines, risks of data hallucination, limited transparency, and compliance uncertainties due to the absence of specific frameworks from regulatory bodies like the FDA, EMA, and ICH. Ethical concerns such as algorithmic bias, patient data privacy, and accountability further complicate deployment. To overcome these issues, hybrid human-AI workflows, explainable AI (XAI), and ethical oversight are recommended, along with domain-specific model training, risk-based regulatory classifications, and global harmonization of standards. Ultimately, the future of AI in pharma depends on balancing innovation with compliance to ensure patient safety, equity, and trust.