Post-Market Surveillance of Medical Devices Using AI
Tushar Khinvasara, Nikolaos Tzenios, Abhishek Shankar
Abstract
Artificial intelligence (AI) has emerged as a transformative tool in post-market surveillance (PMS) for monitoring the safety and performance of medical products. This article explores the role of AI in optimizing PMS practices, focusing on its applications in signal detection, risk assessment, and regulatory compliance. By harnessing machine learning algorithms and big data analytics, AI facilitates the automated analysis of real-world evidence, including patient outcomes data, adverse event reports, and electronic health records. Through pattern recognition and anomaly detection, AI algorithms enable the early identification of potential safety issues and facilitate timely interventions to mitigate risks. Moreover, AI-driven PMS systems enhance regulatory oversight by providing regulators with comprehensive and actionable insights into product safety profiles and emerging trends. However, concerns regarding data privacy, algorithm bias, and interpretability underscore the need for transparent and ethically responsible AI deployment in PMS frameworks.