Application of Big Data and Artificial Intelligence (AI) in pharmaceutical policy and regulatory reform
- VernacularTitle:Эмийн бодлого ба хяналтын шинэчлэлд их өгөгдөл (Big Data) болон хиймэл оюун ухаан (AI)-ы хэрэглээ
- Author:
Gerelt-Od Byambasuren
1
;
Amarjargal Altantsetseg
1
;
Altanbat Ariuntsetseg
1
;
Munkhnasan Enkhsaikhan
1
;
Munkhbat Batbyamba
1
;
Batsukh Tserendolgor
1
Author Information
1. Department of Pharmaceutical Management and Organization, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia
- Publication Type:Review
- Keywords:
Big Data;
Artificial Intelligence;
Pharmaceutical Policy;
Pharmacovigilance;
Real World Evidence;
Mongolia;
NLP
- From:
Mongolian Pharmacy and Pharmacology
2026;28(1):113-116
- CountryMongolia
- Language:Mongolian
-
Abstract:
Abstract:Over the past decade, Big Data and Artificial Intelligence (AI) have emerged as transformative forces in
healthcare, particularly in pharmaceutical policy, regulation, and drug safety surveillance. These technologies enable the systematic collection, integration, and analysis of large-scale and heterogeneous data, thereby strengthening evidence-based decision-making across the pharmaceutical lifecycle. International experience demonstrates that the integration of AI and Big Data supports early detection of adverse drug reactions (ADRs), enhances pharmacovigilance systems, automates regulatory processes, and accelerates drug discovery and development. AI-driven methods,
including machine learning and natural language processing (NLP), facilitate the identification of latent patterns and risk signals from diverse data sources such as clinical trials, Real World Evidence (RWE), electronic health records, and spontaneous reporting systems.
Despite these global advances, the application of AI and Big Data within Mongolia’s pharmaceutical policy and regulatory framework remains limited. Key challenges include fragmented and poorly integrated data sources, insufficient data standardization, underdeveloped information technology infrastructure, limited human resource capacity, and gaps in legal and ethical governance. International initiatives offer valuable reference models for addressing these challenges. The World Health Organization’s VigiBase employs AI and NLP techniques to identify global ADR trends, the U.S. Food and Drug Administration’s Sentinel Initiative integrates RWE to support proactive drug safety monitoring, and the Republic of Korea’s Disease Control and Prevention Agency has implemented an
NLP-based pharmacovigilance system for automated risk detection.
This study aims to assess the applicability of Big Data and AI in Mongolia’s pharmaceutical policy and regulatory system through a comparative analysis of international best practices and the identification of key implementation gaps. The findings emphasize the need for integrated data systems, harmonized standards, localized AI application models, and robust regulatory and ethical frameworks. Overall, the study highlights the potential of Big Data and AI to modernize pharmaceutical policy and regulation in Mongolia and provides evidence-based directions for sustainable implementation aligned with international best practices.
- Full text:2026080200565472636MPPJ-2026-28(1)-113-116.pdf
