To improve epidemic surveillance using AI, we can integrate it with current public health systems. We should update our systems to handle big data sets and ensure AI tools can easily share data with public health databases. We should also train healthcare workers to use AI effectively and create user-friendly interfaces. Using advanced machine learning algorithms that keep learning from new data can help us detect outbreaks more accurately and quickly. By working together, tech companies, healthcare providers, and government agencies can use these AI systems, ensuring they meet public health surveillance’s specific needs.
Using AI for public health readiness has many advantages. AI can quickly analyze large amounts of data, finding patterns and irregularities that traditional methods might miss. This can help predict disease spread and improve resource allocation and emergency planning. However, implementing AI presents challenges, such as ensuring data privacy and quality and addressing potential biases. Setting up AI systems and training personnel can also be costly. Overcoming these challenges requires a strategic approach, including strong data governance, ongoing training, and investment in reliable AI technologies.