Exploring AI Driven Autonomous Vehicles as Mobile Health Units to Expand Access to Healthcare in Underserved Communities
Access to quality healthcare remains a persistent challenge in many low- and middle-income countries, especially for rural and underserved populations. This paper explores the transformative potential of autonomous vehicles (AVs) as mobile health units, proposing a new model for healthcare delivery that leverages emerging transportation technology. We examine how AVs can be equipped with diagnostic tools, telemedicine capabilities, and essential medical supplies to bridge existing gaps in health access. Drawing from current use cases in mobile clinics and early AV deployments, we analyze the opportunities and limitations of this approach through a public health lens. Key barriers such as infrastructure deficits, digital inequity, policy uncertainty, and ethical concerns are critically discussed. The paper also provides targeted recommendations to policymakers, health institutions, and private sector stakeholders on how to advance AV-based healthcare solutions in an inclusive and sustainable manner. By integrating technology with health equity goals, AVs offer a novel and scalable path toward universal health coverage, particularly in contexts where traditional service delivery falls short. This work contributes to the growing conversation on how innovation can reshape public health systems in the 21st century.
Authors
- Ademola Aiyenuro (ORCID: https://orcid.org/0000-0001-8909-4194)
- Adewunmi Akingbola (ORCID: https://orcid.org/0000-0001-7245-0340)
- Olajide Ojo
- Otumara Urowoli Jessica
- Adegbesan Abiodun
- Uthman Hassan Alao
- Petra Mariaria
Institutions
- University of Tasmania (AU)
- University of the West of England (GB)
- University of Ibadan (NG)
- University of Cambridge (GB)
- NYU Langone Health (US)
- Infectious Diseases Institute (UG)
- Anglia Ruskin University (GB)
- Makerere University (UG)
Publication Details
- Journal
- Journal of Epidemiology and Global Health
- Published
- 2026-03-23
- DOI
- https://doi.org/10.1007/s44197-026-00547-6
- Primary Topic
- Human-Automation Interaction and Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.00