New research indicates that artificial intelligence, particularly systems leveraging large language models (LLMs) and natural language processing (NLP), can play a crucial role in preventing future pandemics. Traditional epidemic intelligence relies on manually reported data, which often results in delays and incomplete information, especially in areas with poor healthcare infrastructure. AI-driven systems can analyse diverse datasets in real-time, overcoming these limitations.
AI's ability to sift through vast amounts of data allows for early detection of disease outbreaks. These systems can identify patterns and anomalies that might be missed by conventional surveillance methods. By incorporating real-time policy and genomic data, AI models can accurately forecast infection patterns and hospitalisation trends. Furthermore, AI can assist in resource allocation, optimise healthcare responses, and support informed public health strategies.
However, challenges remain in ensuring data quality, addressing biases, and promoting interdisciplinary collaboration. For AI systems to be effective, they need to be widely used and integrated into public health departments. Despite these challenges, AI offers a promising approach to enhance early warning capabilities, improve forecasting accuracy, and strengthen overall pandemic preparedness.
