The integration of Artificial Intelligence (AI) and Information Systems (IS) is fundamentally reshaping the public health landscape, offering groundbreaking solutions for disease prevention, management, and surveillance. As these technologies advance, they offer unprecedented opportunities to improve health outcomes through real-time monitoring, the creation of personalized health strategies, and greater efficiency across health interventions. Despite these promising developments, the public health sector continues to face ongoing questions regarding the optimal implementation of these tools across diverse populations and complex healthcare systems. This Research Topic was established to explore how the fusion of AI and IS can transform and elevate the efficacy of public health practices, policies, and education.A primary hurdle in leveraging technology is ensuring that health systems are structurally ready for digital adoption. Two key studies in this collection address this "readiness" from different angles. Snowdon et al. contribute a crucial piece. Their work utilizes a cross-sectional analysis to assess the current capacity of digital public health systems, providing a benchmark for how state-level infrastructures can evolve to meet modern technological demands.While infrastructure is physical, "literacy" is the human component of readiness.Kumar et al. explore the barriers preventing healthcare professionals and systems from fully embracing AI. Their research emphasizes that the effectiveness of technological tools depends on the literacy of those who deploy them, identifying specific adoption challenges within the public healthcare sector.One of the most powerful applications of AI in public health is its ability to predict future crises. Mingyu Du explores this topic by utilizing information systems to process time-series data. This study demonstrates how proactive forecasting can provide public health officials with the lead time necessary to outbreaks before they escalate.In addition to infectious diseases, chronic conditions represent a significant global health burden that requires continuous monitoring. Haiyan Xie introduces a specialized technical approach. This study demonstrates how sophisticated neural network architectures, such as capsule networks, can improve the accuracy of predictions for long-term health issues in both clinical and community settings. Complementing this specific model is the systematic review by Liu and Wang. Their review synthesizes current research on combining Internet of Things (IoT) devices and machine learning to provide a comprehensive framework for patient monitoring.Tailoring AI and IS to specific life stages is a recurring theme in this collection.Maternal health, for instance, benefits significantly from data-driven management. At the other end of the life span, the aging population requires intelligent systems for better health management. Wenjie Li et al. address this issue. By leveraging hypergraph convolution, a complex AI technique, they developed a platform capable of addressing the multifaceted needs of the elderly, helping bridge the gap in geriatric care.Monitoring and influencing health behaviors is another area where AI excels. In educational environments, Lu and Ruijuan explore the use of monitoring systems. This research highlights how AI can recognize physical actions to monitor student health and activity levels within schools.Cui and Yin address the psychological barriers to physical activity. Their use of explainable AI is particularly noteworthy, as it helps identify why students who intend to exercise often fail to do so, providing actionable insights into behavioral market. This study raises important questions about equity, examining whether robust governance and higher education can shield vulnerable populations from the potential negative economic impacts of AI.The 14 articles presented in this Research Topic demonstrate the vast potential of Information Systems and Artificial Intelligence in modernizing public health. From digital maturity assessments in Missouri to secure blockchain transactions for IoT, these studies provide a roadmap for a more data-driven health sector. However, the editor notes that significant challenges remain, particularly regarding data privacy and security, as well as the equitable distribution of these benefits.The findings in this special issue underscore that the successful implementation of AI in public health requires more than advanced algorithms; it also necessitates robust infrastructure, high levels of eHealth literacy, and a commitment to security. By addressing these themes, this collection contributes to a deeper understanding of how these powerful tools can be leveraged to foster a healthier and more resilient society
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