1. Implementing AI technologies in epidemic surveillance systems can be transformative in low-resource settings. Using AI can overcome the limitations of weak health infrastructure by processing open-source data to detect early signs of disease outbreaks. The application of mobile technology for data collection and integration of AI tools that are tailored to local languages and contexts would be crucial to enhance AI system use if I consider based on my standpoint. It can also improve by integrating advanced natural language processing (NLP) for better data processing and interpretation. Building partnerships with international health organizations can provide the necessary support and expertise to implement and maintain the systems.
2. I specifically think utilizing AI may provide early detection by the ability to predict disease outbreaks, model the disease spread even in vast datasets, and enhance communication strategies by optimizing limited healthcare resources. It will also support critical insights into disease patterns and potential hotspots, enabling targeted interventions.
Many things to consider in implementing AI systems, infrastructure and technological constraints could be the main considerations, especially in low-resource settings. To ensure data reliability and accuracy, ethical concerns, overcoming resistance from healthcare professionals to adopt AI technologies, and receiving the community’s trust can be also challenges. Significant investment in the infrastructure, inclusive training, regular monitoring, and collaboration among professionals will be needed for the successful implementation of AI in public health preparedness.