AI-Enabled IoT Mobile Health Platform for Telemedicine and Predictive Diagnostics in Underserved Communities in Rural Nigeria

Abstract

AI-enabled IoT mHealth could expand telemedicine and risk stratification in rural Nigeria, but implementation depends on infrastructure, awareness, trust, and clinical governance. Methods: A cross-sectional survey of 1,000 respondents from six rural communities assessed mobile access, telemedicine and IoT awareness, internet and electricity availability, willingness to use AI, trust, healthcare satisfaction, and perceived usefulness. Wilson 95% confidence intervals summarized binary outcomes. Results: Phone ownership was 74.1%, willingness 60.3%, and perceived usefulness 76.8%, whereas reliable internet was 20.0%, telemedicine awareness 26.9%, IoT awareness 17.9%, electricity availability 32.9%, and AI trust 50.0%. Poor or very poor healthcare access was reported by 59.5%. Conclusion: Social receptivity exceeds technical readiness. Deployment should prioritize offline-first telemedicine, clinician oversight, selective IoT integration, and local validation before predictive models are used clinically. Keywords: Artificial intelligence; Internet of Things; mobile health; telemedicine; predictive diagnostics; edge computing; rural healthcare; Nigeria.