International Research Journal of Innovations in Engineering and Technology (IRJIET) ISSN (online): 2581-3048 Volume 8, Issue 9, pp 194-197, September-2024 https://doi.org/10.47001/IRJIET/2024.809024 © 2024-2017 IRJIET All Rights Reserved www.irjiet.com 194 Enhancing Drug Safety: AIs Role in Pharmacovigilance and Adverse Event Reporting Hannah Alex School of Pharmacy, University of Pittsburgh, USA Abstract - The role of pharmacovigilance (PV) in healthcare is to optimize the safety and efficacy of the delivery of pharmaceutical drugs and medical equipment. The increasingly dynamic nature of adverse drug reactions and pharmacovigilance has rendered traditional approaches susceptible to the underreporting of ADRs. Subsequently, the integration of Artificial intelligence in adverse drug reaction reporting is an outstanding technological advancement in pharmacovigilance. Therefore, this critical analysis applied a systematic literature review to comprehend the extensive role of AI in Pharmacovigilance. The research findings acknowledged that AI technologies such as machine learning, deep learning, and natural learning processing (NLP) have automated PV, leading to enhanced signal detection, analysis of unstructured data, risk assessment, and regulatory compliance. Keywords: Pharmacovigilance (PV), natural learning processing, adverse drug reactions (ADR), signal detection, deep learning, Artificial intelligence, and machine learning. I. INTRODUCTION In a field as complex as medicine, the role of AI is inevitable. Pharmacovigilance (PV) and Adverse Event Reporting (AER) are meant to help physicians “do no harm” (Bate and Stegmann 20). These concepts are dedicated to monitoring the impacts of pharmacological drugs after licensure and involve continuous surveillance of the side effects, besides analyzing voluminous scopes of data to find existing and developing unknown side effects (Vickers-Smith et al. 203). Therefore, the voluminous scope of data, high degree of uncertainty, and need to learn from data justify the relevance and applicability of AI in PV and AER. Subsequently, this analysis applies a systematic literature methodology to extensively and critically evaluate the role of AI in Pharmacovigilance and Adverse Event Reporting (AER). II. LITERATURE REVIEW Existing studies have also compared the effectiveness of AI-based and traditional approaches in PV, monitoring and reporting of Adverse drug reactions. For example, in predictive analytics tasks such as identifying complex patterns in Symptoms data and pharmacoepidemiology, AI-based approaches were more reliable than traditional PV methods (Bate and Stegmann 23). Notably, a systematic review of 44 studies was undertaken by Sessa et al. to compare the application of AI and traditional approaches for tasks such as predicting the occurrence or severity of Adverse drug responses and propensity scores (p.121). For instance, despite not performing quality assessments for studies, AI outperformed traditional mechanisms in 50% of the studies Multiple studies have also attempted to demonstrate the complexity and costs involved in individual case safety reports (ICSRs) to justify the implementation of AI in PV and AER. For example, a 2021 GSK study concluded that the estimated average cost of processing an individual case report was $33 and even higher across pharmaceutical companies (Beninger et al. 1229; Stergiopoulos et al. 506). In 2021, Navitaspvnet and pvconnect also conducted an annual survey of pharmaceutical companies' cost per ICSR processing (Bate and Stegmann 23). The findings showed the median was US$86 for Pvnet and $345 in Pvconnect’s survey (Bate and Stegmann 24). Consequently, such processing costs have increased the allure of AI automation regardless of the high initial implementation costs. Besides, the evolving nature of the pharmaceutical landscape is characterized by the increased volume and complexity of the new drugs and therapeutics available annually. The continuous introduction of different drugs has also increased the scope of advanced events reported, prompting the need for technological integration for quality, real-time, convenient and automated drug safety monitoring. For example, Patel et al. point out a 300% increase in the adverse events reported by the FDA Adverse Event Reporting System (FAERS) between 2009 and 2019, further increasing to 2.2 million AE reports by 2021. III. PROPOSED METHODOLOGY The proposed research methodology was a systematic literature review of research articles published between 2010 and 2024. This proposed methodology has also been previously adopted in similar studies examining the role of AI in PV, such as those by Sessa et al. and Bate and Stegmann.