3066-280XISSN (Online)
Volume 2 , Issue 1 , PP: 14-27, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
S. K. Towfek 1 * , Mohamed Elkanzi 2
Doi: https://doi.org/10.54216/MOR.020102
AI and the development of the ML system are expected to play a crucial role in preventing and controlling infectious diseases as part of global health issues. Typically, conventional epidemic models give a narrow perspective of the distribution of diseases and their causes, which leads to the use of AI/ML solutions. Some of these tools utilize genomic data and environmental and patient information to boost forecasts' accuracy and facilitate real-time disease surveillance. The human-driven models of pandemic identification were replaced by sophisticated artificial intelligence models such as deep learning and advanced neural networks indicating patterns, the possibility of future outbreaks, and driving the concept of public health interventions. Many examples can be provided to support the efficiency of ML's approaches to combating antimicrobial resistance, tuberculosis relapse, and the spatial-temporal modeling of an alternative disease such as measles or COVID-19; nonetheless, data standardization, scaling, ethics, and bias issues are limitations to the application of such solutions. Controlling unfairness consists of the problem of transparency, patient data confidentiality, and disparities in the deployment of AI systems. However, practical and comparable implementations of these systems require cross-sector cooperation and global data sharing for varied conditions in the broader healthcare environment. Future developments point to the opportunity to enrich epidemic prediction models by blending genomic precision systems, explainable artificial intelligence, and interdisciplinary studies. This review provides evidence for how AI/ML has revolutionized infectious disease management, calls for responsible innovation and ethical deployment of AI, and encourages international collaborations to safeguard the global health sector against new and emerging diseases. Subsequently, unexpected events with high fatality rates and global impact, such as disease outbreaks, epidemics and pandemics, are still a threat to life; therefore, the ability of AI and ML to advance epidemic preparedness and response in the future is promising to enhance global health protection to future pandemics.
Artificial intelligence , Machine learning , Epidemic prediction , Infectious diseases , Public health , Data integration
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