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Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined
Rogério Caixinha Algarvio1,2, Jaime Conceição1,2,3, Pedro Pereira Rodrigues4
1Faculty of Sciences and Technology, University of Algarve, Faro, Portugal.
International Journal of Clinical Pharmacy
|July 30, 2025
Summary
Artificial intelligence (AI) enhances pharmacovigilance by automating tasks and improving data analysis for adverse drug reactions (ADRs). While promising, AI integration faces challenges like data quality and regulatory hurdles.
Area of Science:
- Pharmacovigilance and Drug Safety
- Artificial Intelligence in Healthcare
- Biomedical Data Science
Background:
- Pharmacovigilance is crucial for monitoring adverse drug reactions (ADRs) and ensuring medication safety.
- Traditional pharmacovigilance methods are often slow and inconsistent.
- Artificial intelligence (AI) offers enhanced efficiency and accuracy in managing complex drug safety data.
Purpose of the Study:
- To explore practical applications of AI in pharmacovigilance, focusing on efficiency, process acceleration, and automation.
- To examine the impact of an expert-defined Bayesian network on causality assessment in a pharmacovigilance center.
Main Methods:
- A comprehensive narrative literature review was performed using MEDLINE, Scopus, and Web of Science.
- Keywords included "pharmacovigilance", "artificial intelligence", "adverse drug reactions", and "drug safety".
- Studies were analyzed without restrictions on publication year or language, with the search conducted in January 2025.
Main Results:
- AI significantly improves pharmacovigilance through streamlined signal detection, surveillance, and automated ADR reporting.
- Techniques like data mining and automated signal detection expedite safety signal identification and enhance data precision.
- Predictive models anticipate ADRs and drug-drug interactions, while a Bayesian network optimized causality assessment, reducing processing time from days to hours.
Conclusions:
- AI demonstrates substantial potential to enhance pharmacovigilance practices and drug safety evaluations.
- Practical integration of AI is currently limited by data quality, regulatory barriers, and algorithm transparency.
- Further development is needed to overcome these challenges for widespread AI adoption in pharmacovigilance.
Keywords:
Artificial intelligenceDrug-related side effects and adverse reactionsMachine learning data miningNatural language processingPharmacovigilanceMore Related Videos
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