Practices that show evidence of effectiveness in improving public health outcomes in a specific real-life setting, as indicated by achievement of aims consistent with the objectives of the activities, and are suitable for adaptation by other communities.

Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review

Naseem, M., Akhund, R., Arshad, H.,Ibrahim, M.T.

Release Date:

Systematic Review/Meta-Analysis

Data Collection and Analysis
Healthcare Access and Quality
Tools Included
Outside U.S.

Data Collection and Reporting

This article explores how health data technology tools such as Artificial Intelligence (AI) and Machine Learning (ML) tools can be implemented and adapted to assist in better responses and outcomes to the COVID-19 pandemic, as well as future epidemics. This literature review focuses on peer-reviewed articles concerning four themes: COVID-19 and the need for AI; utility of AI in COVID-19 screening, contract tracing, and diagnosis; use of AI in COVID-19 patient monitoring and drug development; AI beyond COVID-19 and opportunities for Low-Middle Income Communities (LMIC). This review contains examples of ways healthcare systems have implemented AI and ML to predict and treat outcomes of COVID-19, as well as potential capacities for AI.

Resource Details

Outcomes of Interest

Improve Data Infrastructure

Priority Population(s)

Setting(s) of Implementation


Geographic Area of Implementation

Implementation Period