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Towards AI-Enabled Multimodal Diagnostics and Management of COVID-19 and Comorbidities in Resource-Limited Settings.
- Source :
- Informatics; Dec2021, Vol. 8 Issue 4, p63-63, 1p
- Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 22279709
- Volume :
- 8
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Informatics
- Publication Type :
- Academic Journal
- Accession number :
- 154424623
- Full Text :
- https://doi.org/10.3390/informatics8040063