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Concept Paper

Towards AI-Enabled Multimodal Diagnostics and Management of COVID-19 and Comorbidities in Resource-Limited Settings

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Department of Information Technology, Cape Peninsula University of Technology, Cape Town 8000, South Africa
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Department of Global Health, Stellenbosch University, Cape Town 7505, South Africa
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Faculty of Health Sciences, University of Pretoria, Pretoria 0028, South Africa
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St. Pölten University of Applied Sciences, 3100 St. Pölten, Austria
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Health Information System Program, Pretoria 0181, South Africa
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Envisionit Deep AI, Johannesburg 2196, South Africa
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Gknowmix (Pty) Ltd., Bellville 7530, South Africa
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Division of Chemical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University and National Health Laboratory Service, Tygerberg Hospital, Cape Town 8000, South Africa
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Empirica GmbH, D-53111 Bonn, Germany
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Department of Computer and Information Sciences, Covenant University, Ota 1023, Nigeria
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Author to whom correspondence should be addressed.
Academic Editor: Kamran Sedig
Received: 6 September 2021 / Accepted: 15 September 2021 / Published: 23 September 2021
(This article belongs to the Section Health Informatics)
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 AI-enabled 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 self-data 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. View Full-Text
Keywords: artificial intelligence; COVID-19; resource-limited settings; multimodal diagnostics; diagnostics; machine learning; Explainable AI; point-of-care artificial intelligence; COVID-19; resource-limited settings; multimodal diagnostics; diagnostics; machine learning; Explainable AI; point-of-care
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MDPI and ACS Style

Daramola, O.; Nyasulu, P.; Mashamba-Thompson, T.; Moser, T.; Broomhead, S.; Hamid, A.; Naidoo, J.; Whati, L.; Kotze, M.J.; Stroetmann, K.; Osamor, V.C. Towards AI-Enabled Multimodal Diagnostics and Management of COVID-19 and Comorbidities in Resource-Limited Settings. Informatics 2021, 8, 63. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040063

AMA Style

Daramola O, Nyasulu P, Mashamba-Thompson T, Moser T, Broomhead S, Hamid A, Naidoo J, Whati L, Kotze MJ, Stroetmann K, Osamor VC. Towards AI-Enabled Multimodal Diagnostics and Management of COVID-19 and Comorbidities in Resource-Limited Settings. Informatics. 2021; 8(4):63. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040063

Chicago/Turabian Style

Daramola, Olawande, Peter Nyasulu, Tivani Mashamba-Thompson, Thomas Moser, Sean Broomhead, Ameera Hamid, Jaishree Naidoo, Lindiwe Whati, Maritha J. Kotze, Karl Stroetmann, and Victor C. Osamor 2021. "Towards AI-Enabled Multimodal Diagnostics and Management of COVID-19 and Comorbidities in Resource-Limited Settings" Informatics 8, no. 4: 63. https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040063

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