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Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology.

Daniel J Mollura¹, Melissa P Culp¹, Erica Pollack¹

¹From RAD-AID International, 8004 Ellingson Dr, Chevy Chase, MD 20815 (D.J.M., M.P.C., E.P., G.B., J.R.S., V.L.M., A.E., A.S., F.D.); Department of Radiology and Medical Imaging, Denver Health and Hospital Authority, Denver, Colo (E.P.); Departments of Radiology and Global Health, University of Washington, Seattle, Wash (J.R.S.); Fred Hutchinson Cancer Research Center, Seattle, Wash (J.R.S.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (V.L.M.); Department of Radiology, University of Pennsylvania Health System, Philadelphia, Pa (A.E.); and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (F.D.).

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Summary

This summary is machine-generated.

Resource-poor health institutions can adopt artificial intelligence (AI) for medical imaging through a strategic, integrated approach. This strategy focuses on education, infrastructure, and phased AI introduction to improve global health equity.

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Area of Science:

Medical Imaging
Artificial Intelligence
Global Health Radiology

Background:

Resource limitations in healthcare settings hinder AI adoption for medical imaging.
Lack of diverse data, opaque algorithms, and limited institutional participation affect AI trustworthiness in global health.
Existing disparities are exacerbated by these challenges.

Purpose of the Study:

To present a strategy for AI adoption in resource-poor health institutions.
To address challenges in implementing AI for medical imaging in low-resource settings.
To leverage AI for improving global health equity.

Main Methods:

A three-pronged integrated strategy: clinical radiology education, infrastructure implementation, and phased AI introduction.
Leveraging over a decade of experience in developing radiology in resource-poor settings.
Global health collaborations for AI introduction, testing, and scaling.

Main Results:

The strategy enhances local radiology personnel expertise through comprehensive education.
It establishes necessary infrastructure (software, hardware, networking) for AI integration.
Phased AI introduction, testing, and scaling are facilitated through collaborations.

Conclusions:

The integrated strategy provides a foundation for addressing healthcare disparities.
It enables resource-poor institutions to adopt AI for medical imaging.
This approach supports equitable access to advanced medical technology in global health.