The Potential of Artificial Intelligence Tools in Early Detection of Epidemics: A Comparative Evaluation of International Systems and Localization Strategies Through A Narrative Review

Authors

  • Seyed Ali Mousavi Department of Epidemiology and Biostatistics, School of Medicine, Kurdistan University of Medical Sciences, Sanandaj, Iran
  • Ghobad Moradi Professor of Epidemiology, Social Determinants of Health Research Center, Health Development Research Institute, Kurdistan University of Medical Sciences, Sanandaj, Iran
  • Meysam Abshenas jami Department of Epidemiology and Biostatistics, School of Medicine, Kurdistan University of Medical Sciences, Sanandaj, Iran
  • Elham Nouri Department of Epidemiology and Biostatistics, School of Medicine, Kurdistan University of Medical Sciences, Sanandaj, Iran
  • Atefeh Zahedi Department of Epidemiology and Biostatistics, School of Medicine, Kurdistan University of Medical Sciences, Sanandaj, Iran
  • Yousef Moradi Associate Professor of Epidemiology, Health Metrics and Evaluation Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran

DOI:

https://doi.org/10.18502/ijre.v22i2.22728

Keywords:

Artificial intelligence, Surveillance, Disease outbreaks

Abstract

Background and Objectives: Artificial intelligence (AI) has emerged in recent years as a transformative technology in disease surveillance systems and has considerable potential to improve the early detection of disease outbreaks. This narrative review was conducted with the aim of identifying, comparing, and comparatively evaluating international AI-based surveillance tools, as well as examining the possibility of their localization and adaptation to Iran's health system.

Methods: Scientific publications and national and international reports focusing on active early warning systems were searched and reviewed. The identified tools were evaluated based on their data sources, analytical methods, detection timeliness, interoperability with national surveillance systems, and infrastructure requirements.

Results: The findings showed that despite the availability of electronic health data systems in Iran, including SIB, Sina, and NAB, the absence of an integrated national platform capable of intelligent real-time data processing remains a major challenge. Among the evaluated tools, HealthMap was identified as a more suitable model for developing national surveillance systems because of its reliance on open data, flexible architecture, and compatibility with existing infrastructures. Furthermore, diseases with more structured surveillance systems in Iran, including tuberculosis, HIV infection, COVID-19, and influenza, are recommended as appropriate candidates for pilot implementation of national early detection systems.

Conclusion: The findings highlight the importance of aligning artificial intelligence technologies with local needs, data capacities, and the epidemiological requirements of the country. They also indicate that the development of an intelligent early warning system could play a significant role in improving the performance of Iran’s disease surveillance system.

Published

2026-09-20

Issue

Volume (22) issue (2)

Section

Articles