Daramola, OlawandeNyasulu, PeterMashamba‑Thompson, Tivani PhosaMoser, ThomasBroomhead, SeanHamid, AmeeraNaidoo, JaishreeWhati, LindiweKotze, Maritha J.Stroetmann, KarlOsamor, Victor Chukwudi2022-06-102022-06-102021-09-23Daramola, O.; Nyasulu, P.; Mashamba-Thompson, T.; Moser, T.; Broomhead, S.; Hamid, A.; Naidoo, J.; Whati, L.; Kotze, M.J.; Stroetmann, K.; et al. Towards AI-Enabled Multimodal Diagnostics and Management of COVID-19 and Comorbidities in Resource-Limited Settings. Informatics 2021, 8, 63. https://DOI.org/10.3390/informatics8040063.2227-970910.3390/informatics8040063https://repository.up.ac.za/handle/2263/85775A 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.en© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Multimodal diagnosticsDiagnosticsMachine learningExplainable AIPoint-of-careCOVID-19 pandemicCoronavirus disease 2019 (COVID-19)Artificial intelligence (AI)Resource-limited settings (RLS)Polymerase chain reaction (PCR)Sub-Saharan Africa (SSA)Article