Role of Artifical Intelligence in Pharmacovigilance

Abstract

Pharmacovigilance (PV) focuses on detecting, assessing, understanding, and preventing adverse drug reactions (ADRs) to ensure drug safety. As the pharmaceutical industry grows, traditional methods, such as clinical trials and manual reporting, are challenged by the increasing complexity of data. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to enhance PV by automating processes, improving signal detection, and analysing vast datasets. AI can identify hidden safety signals, predict risks, and streamline case processing, reducing the workload and enhancing efficiency. Integrating AI into PV involves careful attention to data quality, compliance, and transparency. Future advancements, such as deep learning, real-time pharmacovigilance platforms, and multi-modal data integration, will further improve predictive modelling and drug safety monitoring. Despite the challenges of data complexity and privacy concerns, AI’s role in PV continues to expand, offering new opportunities to safeguard public health and improve patient outcomes. Artificial intelligence (AI) is increasingly transforming pharmacovigilance, the science of monitoring the safety of medications. Traditional methods of detecting adverse drug reactions (ADRs) rely on spontaneous reporting systems, which can be slow and underreport ADRs. AI enhances these processes by enabling more efficient data analysis and improving signal detection. One key application of AI in pharmacovigilance is the use of machine learning algorithms to analyse vast amounts of data from diverse sources, including electronic health records, social media, and clinical trial databases. These algorithms can identify patterns and correlations that may indicate potential safety issues, allowing for faster and more accurate identification of ADRs. Natural language processing (NLP) is another critical AI component that facilitates the extraction of relevant information from unstructured data. By analysing physician notes, patient reports, and online discussions, NLP can uncover insights that traditional methods might overlook, thereby enriching the understanding of a drug’s safety profile.AI can also support risk assessment by predicting potential safety issues based on historical data. Predictive analytics models can forecast the likelihood of adverse events, enabling proactive risk management strategies. Moreover, AI streamlines reporting processes by automating the data entry and compliance checks, reducing the administrative burden on healthcare professionals. This increased efficiency not only speeds up the reporting cycle but also enhances data quality, ensuring that regulators receive timely and accurate information. In summary, AI significantly enhances pharmacovigilance by improving data analysis, accelerating signal detection, and facilitating risk assessment. As AI technologies continue to evolve, their integration into pharmacovigilance practices is likely to lead to more robust drug safety monitoring systems, ultimately contributing to better patient outcomes and enhanced public health safety.