MedOne: A Culturally Adapted AI-Teleconsultation Mobile Health Platform for Enhancing Healthcare Access in Rwanda

Authors

  • Innocent Patrick Ngoga University of Lay Adventists of Kigali
  • Jonathan Ngugi University of Lay Adventists of Kigali
  • Djuma Sumbiri University of Lay Adventists of Kigali

DOI:

https://doi.org/10.70619/vol5iss11pp63-78-660

Keywords:

AI-powered diagnostics, Teleconsultation, Healthcare accessibility, Digital health, Mobile health (mHealth), Machine learning, Natural language processing

Abstract

This study presents the development and evaluation of MedOne, an AI-powered mobile healthcare application designed to improve healthcare accessibility in Rwanda. MedOne integrates AI-driven diagnostic tools with teleconsultation services, aiming to address critical healthcare challenges in resource-limited settings. The research employs a mixed-methods approach involving 247 participants, including healthcare professionals, end users, and administrators. The system incorporates machine learning algorithms for symptom assessment, natural language processing for multi-language support, and cloud-based architecture for scalability. Findings suggest the system could significantly reduce consultation times by 34%, increase rural healthcare consultations by 67%, and achieve a diagnostic accuracy of 78.5%. The system's design incorporates offline functionality, multi-language support, and cultural adaptation for the Rwandan context.

Author Biographies

Innocent Patrick Ngoga, University of Lay Adventists of Kigali

Faculty of Computing and Information Sciences

Jonathan Ngugi, University of Lay Adventists of Kigali

Faculty of Computing and Information Sciences

Djuma Sumbiri, University of Lay Adventists of Kigali

Faculty of Computing and Information Sciences

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Published

2025-11-03

How to Cite

Ngoga, I. P. ., Ngugi, J. ., & Sumbiri, D. . (2025). MedOne: A Culturally Adapted AI-Teleconsultation Mobile Health Platform for Enhancing Healthcare Access in Rwanda. Journal of Information and Technology, 5(11), 63–78. https://doi.org/10.70619/vol5iss11pp63-78-660

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