Community health workers and artificial intelligence-based health monitoring and solutions in rural healthcare in Nigeria

Artificial Intelligence-based health monitoring is helping health workers bridge critical gaps in access, speed, and accuracy. Yet while the promise is huge, the reality in rural settings is mixed marked by both transformative benefits and deep structural challenges. Artificial intelligence is reshaping how healthcare reaches the world’s hardest-to-access communities and community health workers are at the center of making that shift work in rural Nigeria. The paper documented the role of community health workers in adopting and deploying Artificial Intelligence-based health monitoring and solutions in rural Nigerian healthcare. Specifically, it examined how Artificial Intelligence tools influence technology uptake, service delivery and local health outcomes, while identifying the conditions that enable or constrain their use. This paper adopted a narrative review and descriptive assessment of Community Health Worker experiences with Artificial Intelligence tools in relation to service delivery, data quality, and health outcomes in rural communities. A total of 39 peer-reviewed articles were reviewed from databases including PubMed, Google Scholar, and other academic sources. The focus of the paper was on community health workers in rural Nigeria. The findings revealed that when community health workers are equipped with training, devices, and supervision, Artificial Intelligence tools improve early detection, referral accuracy, data quality, and follow-up for conditions such as hypertension, maternal complications, and infectious diseases. Additional benefits include faster decision support at the point of care, reduced travel burden for patients, and real-time data that inform targeted community responses. The paper also identified several challenges including limited digital literacy among community health workers, unreliable electricity and network coverage, high device and maintenance costs, weak data privacy safeguards, algorithmic bias from non-local training data, and resistance from patients and staff who distrust automated recommendations. The study summarily found that while Artificial Intelligence-based health monitoring tools can improve early detection, data quality, and service delivery when community health workers are properly trained and supported, their adoption in rural Nigeria is constrained by challenges such as limited digital literacy, poor infrastructure, high costs, data privacy concerns, and distrust. The study recommended that the government and Non-Government Organisations should provide regular digital skills training, hands-on practice, and supportive supervision for community health workers using Artificial Intelligence tools in rural areas.