I Research : Journal of Pharmacy

By S MOHAMED MUSAMIL

Background: Negative responses to pharmaceutical agents (ADRs) represent a critical global health issue, contributing significantly to morbidity, mortality, and patient discomfort. Conventional drug safety monitoring largely depends on traditional data submission systems, which are frequently hampered by reporting delays, incomplete information, and underreporting. The integration of advanced computational intelligence (AI) offers a transformative solution to enhance the precision, efficiency, and responsiveness of ADR surveillance. Objective: This review seeks to assess the present landscape of AI applications in monitoring ADRs and to examine its potential to revolutionize pharmacovigilance practices. Methods: An extensive literature survey was undertaken using databases such as PubMed, Scopus, and Web of Science, focusing on studies conducted between 2015 and 2025 that employed AI methodologies for ADR detection. In addition, a prototype software application was developed to analyze drug-related ADRs using AI-based input interpretation. Results: AI models, particularly those utilizing automated learning algorithms and computational linguistic modeling, have demonstrated remarkable capabilities in identifying ADRs within large-scale biomedical data. Evidence suggests that AI can uncover latent patterns in electronic clinical records, extract relevant insights from social media, and facilitate automated risk signal identification in pharmacovigilance systems. Nevertheless, challenges related to data inconsistency, lack of standardized frameworks, and ethical considerations persist. Conclusion: AI has the potential to significantly advance real-time, proactive drug safety monitoring. Although initial outcomes are promising, broader adoption will require further clinical validation, regulatory alignment, and the establishment of comprehensive ethical guidelines.