A conceptual artificial intelligence (AI)-enabled framework is presented in this study
involving triangulation of various diagnostic methods for management of coronavirus disease 2019
(COVID-19) and its associated comorbidities in resource-limited settings (RLS). The proposed AIenabled
framework will afford capabilities to harness low-cost polymerase chain reaction (PCR)-based
molecular diagnostics, radiological image-based assessments, and end-user provided information
for the detection of COVID-19 cases and management of symptomatic patients. It will support selfdata
capture, clinical risk stratification, explanation-based intelligent recommendations for patient
triage, disease diagnosis, patient treatment, contact tracing, and case management. This will enable
communication with end-users in local languages through cheap and accessible means, such as
WhatsApp/Telegram, social media, and SMS, with careful consideration of the need for personal
data protection. The objective of the AI-enabled framework is to leverage multimodal diagnostics
of COVID-19 and associated comorbidities in RLS for the diagnosis and management of COVID-19
cases and general support for pandemic recovery. We intend to test the feasibility of implementing
the proposed framework through community engagement in sub-Saharan African (SSA) countries
where many people are living with pre-existing comorbidities. A multimodal approach to disease
diagnostics enabling access to point-of-care testing is required to reduce fragmentation of essential
services across the continuum of COVID-19 care.The APC was funded by NRF, South Africa and implementation of PSGT by the Technology Innovation Agency of South Africa.https://www.mdpi.com/journal/informaticsam2022School of Health Systems and Public Health (SHSPH
