Artificial Intelligence Applications in Public Health: 2nd Edition
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computation-14-00106.pdf
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148.67 KB
Format
Adobe PDF
Checksum (MD5)
18f91bffbf67ae859d75eec890faaf26
Author(s)
Chumachenko, Dmytro
Yakovlev, Sergiy
Date Issued
May 4, 2026
Journal
Computation
Publisher
MDPI
Citation
Chumachenko, D.; Yakovlev, S. Artificial Intelligence Applications in Public Health: 2nd Edition. Computation 2026, 14, 106.
Version
Final published version
Abstract
Artificial intelligence (AI) is assuming an increasingly important role in public health, where the scale, heterogeneity, and temporal dynamics of health-related data often exceed the capacity of conventional analytic approaches. Recent scholarship has shown that AI can strengthen epidemic intelligence, support earlier detection of health threats, improve predictive modeling, and enhance evidence generation for public health decision-making. For example, AI-based systems have been recognized as useful for epidemic monitoring and alerting, particularly when rapid integration of multiple data streams is required [1]. At the same time, broader reviews have emphasized their growing relevance for health policy, service planning, and data-driven governance [2]. The literature makes clear that the expansion of AI in health-related domains must be accompanied by careful attention to interpretability, bias, privacy, accountability, and implementation constraints, especially when computational outputs inform population-level interventions or resource allocation [3]. These considerations underscore that the significance of AI in public health lies not only in technical performance but also in its capacity to produce actionable, trustworthy, and context-sensitive insights for complex health systems.
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/computation14050106
