Traditionally, signal detection relied on manual data reviews, statistical methods, and expert assessments. However, with the explosion of real-world data and adverse event reports, Artificial Intelligence (AI) and machine learning (ML) are revolutionizing how safety signals are identified and managed. This blog explores traditional pharmacovigilance signal detection methods, the emergence of AI-driven approaches, and how regulatory services can help life sciences companies optimize their pharmacovigilance operations.Limitations of Traditional Signal DetectionTraditional pharmacovigilance relies on disproportionality analysis (DPA), Bayesian techniques, and case-by-case assessments. While these methods have been effective, they are time-consuming, resource-intensive, and prone to bias.Key challenges include:Data Overload – The increasing volume of adverse event (AE) reports from various sources (spontaneous reports, literature, electronic health records, social media) makes manual analysis difficult.Latency in Detection – Traditional methods may miss early signals, leading to delayed regulatory actions.Human Bias – Manual assessment can introduce subjective biases, affecting the accuracy of signal identification.Regulatory Compliance Burden – Stringent pharmacovigilance requirements demand faster signal detection and reporting, adding pressure on life sciences companies.