AI in Pharmacovigilance: Enhancing Drug Safety through Intelligent Surveillance
Abstract
Introduction: Pharmacovigilance (PV) plays a key role in the healthcare system through the assessment, monitoring, and discovery of drug interactions and their effects in humans. It concerns the detection, assessment, understanding, and prevention of ADRs. Due to the vast quantities and complexity of data to be analyzed, computational methods that can accurately detect ADRs in a timely fashion have become a critical component in PV. This prompted the exploration of Artificial Intelligence (AI) applications to enhance pharmacovigilance practices.
Aims and Objective: To explore the types and methods of AI technologies in pharmacovigilance, assessing their potential to improve the efficiency, accuracy, and timeliness of adverse event detection and analysis.
Methods: This literature review was done by searching articles in databases like PUBMED, EMBASE, SCOPUS, and COCHRANE database. The search was made using “artificial intelligence”; “pharmacovigilance”; “machine learning”; “Deep learning”; “Adverse drug reaction”; “case reporting” as the keywords.
Results: AI applications in pharmacovigilance offer rapid identification of potential safety signals, early detection of emerging risks, and improved comprehension of intricate drug-event relationships. Methods like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) can be used in pharmacovigilance assessments of drug safety and to understand the causes of side effects. Automation of routine tasks enables pharmacovigilance professionals to dedicate more time to in-depth analysis and decision-making.
Conclusion: The implementation of AI in pharmacovigilance has significant implications for proactive and personalized drug safety monitoring. While AI holds great promise in advancing pharmacovigilance, challenges such as algorithm validation, transparency, and ethical considerations must be addressed. Collaboration among stakeholders is crucial to harness the full potential of AI in enhancing drug safety.