AI-Driven Solutions for Public Health Monitoring

Sangwoo Jeon, Sam Goundar, Jueying Li, S. Gnanamurthy

Abstract


AI-driven solutions are transforming public health monitoring by enabling timely data analysis and response to health crises. This paper examines the methodologies and technologies employed in AI applications for public health, including disease surveillance, predictive modeling, and resource allocation. Through case studies, the paper highlights the benefits of implementing AI-driven public health monitoring systems, such as improved disease detection, enhanced outbreak response, and optimized healthcare resource utilization. Additionally, challenges related to data accuracy, privacy concerns, and inter-agency collaboration are discussed.

Keywords


AI solutions, public health monitoring, disease surveillance, outbreak response.

References


Ahmadi, Ali. “Digital Health Transformation: Leveraging Ai for Monitoring and Disease Management”. International Journal of BioLife Sciences (IJBLS), vol. 3, no. 1, Parnam Publication Co., 2024, pp. 10–24.

Allam, Zaheer, et al. “Artificial Intelligence (AI) Provided Early Detection of the Coronavirus (COVID-19) in China and Will Influence Future Urban Health Policy Internationally”. Ai, vol. 1, no. 2, MDPI, 2020, pp. 156–165.

Chintala, Sathish Kumar. “AI in Public Health: Modelling Disease Spread and Management Strategies”. NeuroQuantology, vol. 20, no. 8, NeuroQuantology, 2022, p. 10830.

De Angelis, Luigi, et al. “ChatGPT and the Rise of Large Language Models: The New AI-Driven Infodemic Threat in Public Health”. Frontiers in Public Health, vol. 11, Frontiers Media SA, 2023, p. 1166120.

Hossain, Md Sazzad, et al. “Leveraging AI for Real-Time Monitoring and Prediction of Environmental Health Hazards: Protecting Public Health in the USA”. Revista de Inteligencia Artificial En Medicina, vol. 15, no. 1, 2024, pp. 1117–1145.

Jo, Eunkyung, et al. “Understanding the Benefits and Challenges of Deploying Conversational AI Leveraging Large Language Models for Public Health Intervention”. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023, pp. 1–16.

Munagandla, Vamshi Bharath, et al. “AI-Powered Cloud-Based Epidemic Surveillance System: A Framework for Early Detection”. Revista de Inteligencia Artificial En Medicina, vol. 15, no. 1, 2024, pp. 673–690.

Schwalbe, Nina, and Brian Wahl. “Artificial Intelligence and the Future of Global Health”. The Lancet, vol. 395, no. 10236, Elsevier, 2020, pp. 1579–1586.

Tshimula, Jean Marie, et al. “Artificial Intelligence for Public Health Surveillance in Africa: Applications and Opportunities”. arXiv Preprint arXiv:2408. 02575, 2024.


Refbacks

  • There are currently no refbacks.


Bookmark and Share


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.