The role of Artificial Intelligence in Global Health Surveillance

This paper examines the role of Artificial Intelligence (AI) in transforming global health surveillance systems, emphasizing its profound impact on disease detection, monitoring, and response capabilities. By leveraging advanced AI technologies such as machine learning and natural language processing, the paper delves into how these tools enhance the efficiency and accuracy of public health strategies. Through a series of case studies, the effectiveness of AI in real-world scenarios is analyzed, showcasing its potential to predict and manage disease outbreaks more effectively than traditional methods. Additionally, the paper addresses the ethical challenges and technical limitations of integrating AI into existing health surveillance frameworks. Recommendations for overcoming these challenges are provided, alongside a discussion on the necessity of robust data protection measures and the development of unbiased AI algorithms. The comprehensive analysis aims to provide stakeholders with insights into both the transformative potential of AI and the pragmatic considerations needed for its responsible implementation in the field of global health surveillance.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-30
DOI
https://doi.org/10.5281/zenodo.15104845
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
0.00
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article

The role of Artificial Intelligence in Global Health Surveillance

Zenodo (CERN European Organization for Nuclear Research)
Data-Driven Disease Surveillance
article

The role of Artificial Intelligence in Global Health Surveillance

article en

Abstract

This paper examines the role of Artificial Intelligence (AI) in transforming global health surveillance systems, emphasizing its profound impact on disease detection, monitoring, and response capabilities. By leveraging advanced AI technologies such as machine learning and natural language processing, the paper delves into how these tools enhance the efficiency and accuracy of public health strategies. Through a series of case studies, the effectiveness of AI in real-world scenarios is analyzed, showcasing its potential to predict and manage disease outbreaks more effectively than traditional methods. Additionally, the paper addresses the ethical challenges and technical limitations of integrating AI into existing health surveillance frameworks. Recommendations for overcoming these challenges are provided, alongside a discussion on the necessity of robust data protection measures and the development of unbiased AI algorithms. The comprehensive analysis aims to provide stakeholders with insights into both the transformative potential of AI and the pragmatic considerations needed for its responsible implementation in the field of global health surveillance.

Zenodo (CERN European Organization for Nuclear Research)
De Montfort University (GB)
Openalex Percentile: Top 56%
Data-Driven Disease Surveillance
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The role of Artificial Intelligence in Global Health Surveillance · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS