Abstract
Vaccination is a key to public health and safety, but adverse vaccine side effects threaten its safety and monitoring. Traditional pharmacovigilance systems are characterised by flaws such as underreporting, delays, and data fragmentation. This paper presents a holistic review of the role of artificial intelligence (AI) in vaccine side effect detection and prediction. Methodologies like machine learning, deep learning, and natural language processing are examined, with a close look at their application to real-world data sources like electronic health records, social media, and vaccine registries. Evidence from the COVID-19, HPV, and influenza vaccines shows that AIs can detect vaccine side effects, with each of the AI models achieving a high accuracy of (AUC 0.91, F1-score 0.903) in vaccine side effect detection. However, despite the promising benefits, some challenges such as data quality, model generalisation, and ethical challenges still exist. This paper also suggests that future research directions should include vaccinology, federated learning, and explainable AI for real-time and personalised risk assessment. Conclusively, this paper brings to the fore the numerous benefits that AI offers in pharmacovigilance, showcasing its transformative potential for proactive vaccine safety surveillance.