Abstract: Predictive modeling in drug safety utilizes artificial intelligence (AI) to proactively identify and mitigate adverse drug reactions (ADRs), significantly enhancing pharmacovigilance efforts. By analyzing massive datasets encompassing patient demographics, medical histories, drug interactions, genetic predispositions, and clinical trial data, AI algorithms can identify intricate patterns and predict potential risks with unprecedented accuracy. This surpasses the limitations of traditional methods, which often rely on retrospective analysis of adverse event reports. 1 Machine learning techniques, such as deep learning and natural language processing, play a pivotal role in extracting valuable insights from diverse data sources, including electronic health records (EHRs), social media, and scientific literature. Deep learning models, for example, can analyze complex medical images and identify subtle biomarkers associated with drug toxicity. Natural language processing enables the extraction of relevant information from unstructured text data, such as clinical notes and patient narratives, allowing for a more comprehensive understanding of ADRs. AI-powered predictive models can forecast the likelihood of specific ADRs in individual patients, identify high-risk populations, and even suggest optimal drug dosages and treatment regimens. 6 This personalized approach to drug safety enhances patient outcomes by minimizing the risk of serious adverse events. Furthermore, AI-driven pharmacovigilance systems can continuously monitor real-world drug usage data, detect emerging safety signals in near real-time, and facilitate rapid regulatory responses. By analyzing large-scale data streams, these systems can identify unexpected safety concerns that may have gone unnoticed by traditional surveillance methods. 8 This proactive approach empowers regulatory agencies to swiftly implement necessary safety measures, such as issuing warnings, modifying drug labels, or even withdrawing a drug from the market if necessary. The integration of AI into drug safety practices has the potential to revolutionize how medications are developed, monitored, and used. By leveraging the power of AI, the pharmaceutical industry can accelerate the development of safer and more effective medications, improve patient outcomes, and enhance the overall safety profile of the global drug supply.
Keywords: Adverse Drug Reactions (ADRs), Pharmacovigilance, Drug Safety, Artificial Intelligence (AI), Machine Learning (ML), Predictive Modeling, Data Science ,Precision Medicine.
|
DOI:
10.17148/IJARCCE.2025.14111
