Research on Public Health Application Strategies for AI-Assisted Early Warning of Infectious Diseases
Abstract
The artificial intelligence (AI)-assisted paradigm has changed the landscape of early warning on infectious diseases; however,
there exist many challenges when applying it into public health. In this paper, we review the present situation of AI-assisted early warning
systems by highlighting critical problems like inadequate multi-source data fusion, lack of model interpretability, and incomplete governance
framework. We then provide countermeasures: optimize data governance system, develop interpretable intelligent models, strengthen policy,
regulatory, and talent support mechanism. Our conclusion shows that to make the leap from technological embedding to systemic integration
requires coordination among data, algorithms, and institutions, making AI a reliable pillar for infectious disease early warning.
there exist many challenges when applying it into public health. In this paper, we review the present situation of AI-assisted early warning
systems by highlighting critical problems like inadequate multi-source data fusion, lack of model interpretability, and incomplete governance
framework. We then provide countermeasures: optimize data governance system, develop interpretable intelligent models, strengthen policy,
regulatory, and talent support mechanism. Our conclusion shows that to make the leap from technological embedding to systemic integration
requires coordination among data, algorithms, and institutions, making AI a reliable pillar for infectious disease early warning.
Keywords
Artificial intelligence; Infectious diseases; Early warning; Public health; Smart multi-point trigger
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[3] Nadarajah N, Amanda M X L, Dempsey K, et al. Education programs on emerging and reemerging infectious disease outbreak management for pre-registration Nursing and Medical Students: A scoping review. Nurse Education Today, 2027, 168: 107368.
DOI: http://dx.doi.org/10.70711/pmr.v3i11.10327
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