Data Collection and Management for AI-Based Pharmaceutical Formulation Development: A Comprehensive Review.
INTRODUCTION Traditional pharmacovigilance relies on slow clinical trials and post-marketing studies with limited coverage. This review synthesizes evidence on Real-World Data (RWD) integration with Artificial Intelligence (AI) for enhanced Adverse Drug Reaction (ADR) detection, evaluates generative AI like ChatGPT-4 and LLaMA-2 in Substance Use Disorder (SUD) scenarios, discusses current applications, and outlines future directions. The objective is to guide researchers, clinicians, and regulators in this evolving field. METHODS Literature was reviewed on RWD sources (EHRs, claims, registries, wearables), AI algorithms (supervised/ unsupervised learning, NLP, deep learning), and regulatory frameworks. Generative AI performance was assessed via clinician-blind evaluation of responses to Reddit-sourced SUD queries from r/stopdrinking, r/leaves, and r/OpiatesRecovery, with fact-checking against SAMHSA/FDA guidelines and consistency testing. Data included tables comparing RWD, algorithms, and AI models. RESULTS AI enables real-time ADR signals via RWD-AI in CCM, improving diagnostics, personalization, and drug discovery. ChatGPT-4 suggested unsafe opioid microdosing; LLaMA-2 referenced nonexistent resources and improper Xanax sharing, both showing severe inaccuracies in SUD contexts. Tables highlight RWD applications, algorithm uses, and AI limitations like bias and inconsistency. DISCUSSION RWD-AI transforms pharmacovigilance but faces bias, transparency, and validation challenges. FHIR/DLT enhance secure exchange; generative AIs require oversight. Implications include equitable safety monitoring via bias mitigation and regulatory compliance. CONCLUSION AI-RWD integration advances ADR detection and personalized safety, despite generative AI risks in SUD management. Future success demands validated LLMs, FHIR/blockchain infrastructure, and clinician collaboration for comprehensive, equitable pharmacovigilance.