Version 1
: Received: 14 July 2023 / Approved: 17 July 2023 / Online: 18 July 2023 (08:59:39 CEST)
How to cite:
Ogbaga, I. Artificial Intelligence (AI)-Based Solution to Malaria Fatalities In Africa: An Exploratory Review. Preprints2023, 2023071133. https://doi.org/10.20944/preprints202307.1133.v1
Ogbaga, I. Artificial Intelligence (AI)-Based Solution to Malaria Fatalities In Africa: An Exploratory Review. Preprints 2023, 2023071133. https://doi.org/10.20944/preprints202307.1133.v1
Ogbaga, I. Artificial Intelligence (AI)-Based Solution to Malaria Fatalities In Africa: An Exploratory Review. Preprints2023, 2023071133. https://doi.org/10.20944/preprints202307.1133.v1
APA Style
Ogbaga, I. (2023). Artificial Intelligence (AI)-Based Solution to Malaria Fatalities In Africa: An Exploratory Review. Preprints. https://doi.org/10.20944/preprints202307.1133.v1
Chicago/Turabian Style
Ogbaga, I. 2023 "Artificial Intelligence (AI)-Based Solution to Malaria Fatalities In Africa: An Exploratory Review" Preprints. https://doi.org/10.20944/preprints202307.1133.v1
Abstract
Malaria remains a primary public health challenge in Africa, and there is growing interest in leveraging artificial intelligence (AI) to raise malaria interventions. This research examines the possible influence, challenges, and recommendations for implementing AI-based personalized malaria interventions in Africa. AI offers several opportunities in malaria management, including early detection and prediction of outbreaks, improved diagnosis, personalized interventions, optimal treatment recommendations, surveillance and response, resource optimization, and research innovation. However, the implementation of AI in malaria interventions faces various challenges. These include data availability and quality, infrastructure and resource constraints, contextual relevance and generalizability, ethical and privacy considerations, integration into healthcare workflows, and the need to build trust and acceptance among stakeholders. To address these challenges, it is recommended to strengthen data infrastructure, build local capacity in AI technologies, contextualize AI models to local settings, address ethical considerations, establish monitoring and evaluation frameworks, promote collaboration and knowledge sharing, and secure sustainable funding and long-term commitment. By considering these recommendations, stakeholders can work towards implementing AI-based personalized malaria interventions in Africa that can contribute to improving malaria control outcomes, reducing the weight of the disease, and advancing public health in the region.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
