KEEPING AN AI ON DISEASE SURVEILLANCE Artificial Intelligence (AI) packs superpowers in data processing and predictive modeling and could transform disease surveillance as we know it. By capturing information from data sources like country reports, social media and hospitals, AI can speed disease detection and improve "contact tracing" efforts during outbreaks. There are potential pitfalls, though. For all of AI’s promise in revolutionizing disease surveillance, a major roadblock in its scale-up could be lack of data collection and data security measures, according to Stephanie McKay and Sulzhan Bali in Global Health NOW. Concerns surrounding missing data are especially valid in low- and middleincome countries that may lack the infrastructure and human capital required to maintain these systems, they note. Scarcity of data, and lack of transparency on algorithms (especially where private companies are involved), could compound challenges of misinformation or false alarms as well. Keys to offsetting the risks could include educating policymakers, integrating AI systems for disease surveillance with traditional surveillance networks, and investing in training programs for data scientists. -Johns Hopkins’ Global Health NOW
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