This paper looks at how digital tools and artificial intelligence are changing the way we watch drug side effects, making it better and more accurate. The research explores how using AI to automate drug safety checks can shift the approach from dealing with problems after they happen to spotting them before they become serious. This helps fix the problems with the old, manual ways of doing things. In the past, pharmacovigilance relied mostly on people manually collecting information and waiting for reports, which led to incomplete data, mistakes, and difficulty keeping up with large volumes of information. Now, with more data coming from places like electronic health records, social media, and medical research, there’s a big need for better ways to track drug safety in real time and accurately assess risks. The study uses various machine learning models, including Gradient Boosting Machines, Random Forests, and Deep Learning, along with Natural Language Processing, to analyze both organized and unorganized data. It also uses Explainable AI and Knowledge Graphs to help explain the results of safety alerts more clearly. The results show that AI can greatly improve the ability to find drug side effects. For example, Gradient Boosting Models have achieved AUC values up to 0.95, which is much better than traditional statistical methods. Natural Language Processing has also shown high accuracy in analyzing patient stories from social media, with F-measures between 0.72 and 0.82. AI tools have also been found to reduce case processing time by up to 50%, helping regulatory bodies and drug companies use their resources more efficiently. The conclusion is that using AI in pharmacovigilance is a major step toward more automated and accurate monitoring of drug safety. Even though there are still some challenges like varied data and possible bias in algorithms, using standard AI techniques is important for better patient care and building trust in both modern and traditional medicine.
Artificial Intelligence, Machine Learning, Pharmacovigilance Accuracy, Signal Detection, Workflow Optimization, Drug Safety Monitoring.
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