Digital Surveillance and Artificial Intelligence in Detection of AEFI: A New Frontier in Vaccine Safety Monitoring - Siddiqui, Soiba; Ramesh, Madhan; Narayana, Prashanth Sathya (International Journal of Pharmaceutical Investigation, 2026)
The integration of digital surveillance and Artificial Intelligence (AI) technology constitutes a significant shift in Adverse Event Following Immunization (AEFI) monitoring. This review explores the artificial intelligence based approaches such as machine learning, Natural Language Processing (NLP), and blockchain technology to improve vaccination safety surveillance. Artificial intelligence provides the immediate and accurate identification of safety signals by using a variety of data sources, such as electronic health records, social media platforms, and mobile health applications. Social media analytics have shown great promise for early detection of AEFIs, but blockchain protects the integrity and transparency of safety data. Despite these advances, difficulties remain, including underreporting, inequities in healthcare infrastructure, and ethical considerations including data privacy and biased algorithms. Low-resource environments, in specifically, experience challenges due to insufficient digital infrastructure and employee abilities. The assessment emphasizes the importance of combining old passive reporting systems with modern digital tools to develop a comprehensive and balanced AEFI monitoring framework. By addressing these barriers enabling global collaboration, digital technologies can increase the effectiveness of AEFI surveillance, increase confidence in vaccination programs, and improve emergency response times. This innovation in vaccine safety monitoring has the ability to address existing gaps and provide a strong framework for global immunization safety.
Keywords: Adverse Event Following Immunization; Artificial Intelligence; Digital Surveillance; Vaccine Safety