Aug 2026· International Journal on Sustainable Industrial Revolution· Vol 1· 0 citations· 68 references
TL;DR
The outcome shows the increasing potentials of artificial intelligence in the provision of strong support and strength for the public health systems as well as in the identification of important challenges like limitation of data, implementations of barriers.
Abstract
The study focused on a systematic review of the application of artificial intelligence in predicting the disease surveillance as well as early warning system in Nigeria. Relevant literatures from the main database were selected, screened and analysed using a predefined criterion. The study assessed the techniques, sources of data and their effectiveness in enhancing the detection and response to disease outbreak early enough. The outcome shows the increasing potentials of artificial intelligence in the provision of strong support and strength for the public health systems as well as in the identification of important challenges like limitation of data, implementations of barriers. Thus, the study provides adequate information into the recent trends, gaps an opportunity for improving the artificial intelligence driven disease surveillance in the country.
Background and Objectives: Artificial intelligence (AI) has emerged in recent years as a transformative technology in disease surveillance systems and has considerable potential to improve the early detection of disease outbreaks. This narrative review was conducted with the aim of identifying, comparing, and comparati...
Seyed Ali Mousavi, Ghobad Moradi, Meysam Abshenas Jami et al.· Iranian Journal of Epidemiol...· 0 citations
Artificial intelligence (AI) could be of assistance in the management of infectious diseases, as well
as in the prevention and early diagnosis of infectious diseases. Additionally, machines that contain
AI are specifically built to work with varying degrees of autonomy during their operation. In
addition, it is a sy...
Dina Darwish· International Journal of Com...· 0 citations
This paper presents a methodological illustration of malaria surveillance in Northern Nigeria using synthetic data, benchmarking five ML models under temporal validation, and employing SHapley Additive exPlanations for robust model interpretability, and proposes a tiered strategic framework encompassing policy, infrast...
L. Aliyu, Abbas B. Umar, Saifuddeen K. Sani et al.· BMC Artificial Intelligence· 1 citation
Artificial intelligence is a promising tool to transform FBD surveillance and its implementation requires dataset standardization, privacy protection, the reduction of algorithmic bias, and harmonized regulatory frameworks.
D. Lipari, E. Modica, A. L. Puma et al.· Journal of Biological Resear...· 0 citations
This policy brief highlights the urgent need for responsible AI governance through international standards, explainable algorithms, independent validation, strong data protection, and inclusive stakeholder engagement to maximize its potential for improving disease surveillance and global health security.
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The feasibility of applying machine learning for public health surveillance in resource constrained environments is confirmed and provides policymakers and healthcare providers with vital tool for mitigating health risk in crude oil host communities through data-driven early warning systems rather than reactive approac...
Olutomisin M. Orogbemi, Balogun John Tope· International Journal of Sci...· 0 citations
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