The COVID-19 pandemic sparked not only a race for effective vaccines but also a wave of discussion – and sometimes misinformation – about potential side effects. Sifting through the mountains of reports, from official channels like VAERS to the bustling world of social media, posed a huge challenge. But what if AI could help? Researchers tackled this problem head-on, investigating whether artificial intelligence could accurately identify and extract reports of adverse events following COVID-19 vaccination. Their work focused on automatically recognizing key entities in text, such as the specific vaccine, the dose number, and the reported adverse event. Traditional deep learning models, known for their ability to find patterns in data, were pitted against powerful large language models (LLMs) like those behind ChatGPT, known for their text comprehension skills. The results? A fascinating blend of strengths and weaknesses. While traditional models sometimes struggled, LLMs showed remarkable promise in accurately identifying these crucial details. The real breakthrough, however, came when researchers combined these different AI approaches into an ensemble. This supergroup of AI models outperformed individual models, demonstrating the power of combining different AI strengths. This points to a future where AI can play a vital role in monitoring public health concerns and responding effectively to emerging issues. However, the study also revealed some limitations. The AI models sometimes missed less frequent or ambiguously described side effects, highlighting the need for improved methods to handle nuanced language. This research underlines the growing potential of AI in healthcare, showing how it can help us better understand vaccine safety and potentially improve public health responses.