Application of Artificial Intelligence in Community Health Surveillance by Health Workers Across Selected PHCs in Benin City, Edo State

This study examined the application of Artificial Intelligence (AI) in community health surveillance among health workers in selected Primary Health Care (PHC) centres in Benin City, Edo State, Nigeria. A descriptive cross-sectional study design was adopted. A total of 230 health workers completed self-administered structured questionnaires, yielding a response rate of 95.8%. Findings showed that more than three-quarters of respondents had good knowledge of AI in disease surveillance, though applied and tool-specific knowledge remained limited. Approximately two-thirds demonstrated positive attitudes toward AI, recognising its potential to improve surveillance efficiency, outbreak detection, and reporting. However, actual uptake was very low, with only a small minority having ever used any AI-based surveillance tool; HealthMap, ChatGPT, and WHO EIOS were the most commonly cited. Use was largely occasional and most users had discontinued. Major barriers to adoption included cost and lack of funding (100%), absence of institutional training (100%), inadequate infrastructure (99.6%), unclear ethical guidelines (69.6%), and data privacy concerns (67.0%). On multivariate analysis, unclear ethical guidelines were the strongest significant predictor of non-uptake. The study concludes that despite reasonable awareness and generally positive attitudes, AI adoption in PHC-based disease surveillance in Benin City remains negligible, constrained primarily by systemic, infrastructural, and governance gaps. It recommends structured AI training, development of ethical and data governance frameworks, investment in digital infrastructure, and institutional support as foundational requirements for AI integration into community health surveillance in Edo State, Nigeria. Artificial Intelligence- Community health surveillance- Disease surveillance- Primary Health Care- Health workers- Nigeria- Edo State- Benin City- Machine learning- Public health- AI adoption- MBBS thesis