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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63700
Volume:
10 |
5 |
May 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Bachelor of pharmacy
Sumantai Institute of pharmacy bambrud
Bachelor of pharmacy
Sumantai Institute of pharmacy bambrud
Bachelor of pharmacy
Sumantai Institute of pharmacy bambrud
Pharmacovigilance plays a critical role in ensuring drug safety through the detection, assessment, monitoring, and prevention of adverse drug reactions (ADRs) [1]. However, the rapid growth of healthcare data, increasing medicine use, and limitations of traditional reporting systems have created severe challenges such as significant underreporting, delayed signal detection, and heavy manual workloads [2]. Artificial Intelligence (AI) has emerged as an innovative solution to strengthen modern pharmacovigilance systems by improving the efficiency, accuracy, and speed of drug safety monitoring [3]. This review report thoroughly examines the application of diverse AI technologies in pharmacovigilance and evaluates their impact on broader healthcare vigilance frameworks [4]. Advanced methods such as Machine Learning (ML), Natural Language Processing (NLP), predictive analytics, and data mining are increasingly utilized to analyze large-scale heterogeneous healthcare databases, extract hidden ADR patterns, and support real-time post-marketing safety surveillance [5]. These computational technologies enhance initial signal detection, automate routine individual case processing, minimize human errors, and profoundly improve clinical and regulatory decisionmaking for healthcare professionals and regulatory authorities [6]. The study highlights how AI-powered systems significantly reduce manual operational workloads while facilitating the early identification of potential drug safety risks [7]. Furthermore, AI improves the active management of vast volumes of clinical, electronic health record, and social media data, leading to more proactive and efficient pharmacovigilance workflows [8]. Despite these undeniable technical advantages, critical challenges such as data privacy concerns, a distinct lack of standardized datasets, algorithm transparency ("black-box" models), ethical issues, and high implementation costs remain key barriers to widespread institutional adoption [9].
KEYWORDS
The integration of artificial intelligence into pharmacovigilance represents a complete paradigm shift from traditional reactive surveillance toward proactive, predictive safety monitoring [123]. AI technologies including machine learning, natural language processing, and Bayesian networks demonstrate significant potential to enhance the efficiency, accuracy, and comprehensiveness of adverse drug reaction detection and assessment [124]. Evidence from systematic reviews indicates that machine learning algorithms generally outperform traditional disproportionality methods, with pooled sensitivity and specificity of 78.1% and 70.6% for development studies, and even higher performance (81.5% sensitivity, 79.5% specificity) in studies incorporating external validation [125]. Despite this massive promise, the widespread implementation of AI in routine pharmacovigilance practice faces significant challenges related to data quality, algorithmic transparency, regulatory acceptance, and ethical considerations [126]. Addressing these limitations will require intense collaborative efforts among researchers, regulators, industry stakeholders, and the healthcare community to develop robust validation frameworks, ensure algorithm fairness, and establish appropriate governance structures [127]. As these challenges are progressively addressed, AI-powered pharmacovigilance systems will play an increasingly vital role in safeguarding patient safety, enabling more personalized risk-benefit assessments, and enhancing public health outcomes in an era of therapeutic complexity and big data [128]. Continued advancements in AI technologies are expected to create smarter and more reliable pharmacovigilance systems that will improve global healthcare outcomes and ensure safer use of medicines worldwide [129].
1. Artificial intelligence (AI) in pharmacovigilance: A systematic review on predicting adverse drug reactions (ADR) in hospitalized patients. Research in Social and Administrative Pharmacy. 2025;21(6):453–462. 2. Artificial intelligence in pharmacovigilance: A narrative review. PMC. 2025;47(4):932–944. 3. Artificial Intelligence: Applications in Pharmacovigilance Signal Management. PMC. 2025;39(3):183–198. 4. Artificial intelligence in pharmacovigilance: advancing drug safety monitoring. PMC. 2025;16:20420986251361435. 5. AI and Big Data for Safety Signal Detection in Pharmacovigilance. BauPharma. 2025. 6. Charting the frontier of healthcare with big data and AI. ScienceDirect. 2025. 7. AI-Powered Signal Detection in Pharmacovigilance. Horizon Europe. 2025. 8. Hu et al. Machine learning for prediction of ADRs in older inpatients. 2024. 9. Nikfarjam et al. Natural language processing for ADR detection from social media. 2025. 10. Yang et al. Deep learning for free-text ADR reports. 2024.
Publication Date
5/21/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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