The field of drug safety, pharmacovigilance and pharmacoepidemiology is experiencing a revolutionary transformation through the integration of artificial intelligence (AI), both in terms symbolic AI structures (e.g. Knowledge Graphs and ontologies) and machine learning (ML). These technologies offer unprecedented capabilities in enhancing the detection, assessment, and comprehension of adverse drug reactions and medication usage patterns. Despite these advancements, the challenge remains in effectively integrating AI tools in a manner that adheres to regulatory standards while maximizing their potential.

The goal of this Research Topic is to explore and elucidate the various methodologies through which AI and ML can be effectively utilized to innovate and advance the fields of drug safety, pharmacovigilance, and pharmacoepidemiology. We aim to highlight cutting-edge technologies, including reasoning, data modelling, data mining, predictive analytics, etc., which can facilitate more robust drug safety monitoring systems and improve healthcare decision-making processes. Through this exploration, the Research Topic will contribute to the ongoing dialogues surrounding ethical considerations, accuracy, and the practical deployment of AI in drug safety analysis.

To further explore the effective utilization of AI within this Research Topic, we invite submissions that address, but are not limited to, the following theme:
• Implementation and validation of AI algorithms in adverse drug reaction detection
• ML techniques for predictive analytics in drug safety
• Knowledge Graphs drug safety
• Role of natural language processing in enhancing data extraction from diverse data sets
• Ethical implications and regulatory considerations for AI in pharmacovigilance
• Case studies and real-world applications of AI in pharmacoepidemiological research: Improving transparency and explainability in ML models used in pharmacovigilance

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Advances in AI for Drug Safety – Updates from 2025