A Retrospective Study of Robotics in Adverse Drug Reaction - Monitoring and Errors

Abstract

Adverse Drug Reactions (ADRs) remain a major concern in healthcare, contributing significantly to patient morbidity, hospital admissions, and healthcare costs worldwide. Traditional pharmacovigilance systems, which rely on spontaneous reporting and retrospective analysis, often face limitations such as underreporting, delayed detection, and inefficiency in handling large-scale data. This review explores the integration of robotics and artificial intelligence (AI) in ADR monitoring systems, highlighting their transformative potential in enhancing drug safety surveillance. AI-driven technologies, including machine learning and natural language processing, enable real-time data analysis, improved signal detection, and predictive modeling using diverse data sources such as electronic health records, clinical trials, and social media. Robotics further enhances efficiency by automating repetitive tasks like data collection, processing, and reporting, thereby reducing human error and workload. The study evaluates various AI methodologies, including supervised and unsupervised learning models, and discusses their applications in signal detection, data processing, and adverse event prediction. Despite these advancements, challenges such as data quality dependency, lack of model transparency, integration barriers, ethical concerns, and regulatory limitations persist. The review also emphasizes future directions, including global data collaboration, personalized pharmacovigilance, integration with pharmacogenomics, and the development of standardized AI regulations. Overall, the integration of robotics and AI in ADR monitoring represents a significant advancement toward proactive, efficient, and accurate pharmacovigilance systems, with the potential to improve patient safety and healthcare outcomes.