How artificial intelligence can help in combating infectious diseases
Article Sidebar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Main Article Content
Abstract
The infectious diseases remain a serious health concern on the planet, and it was especially widely observed during the COVID-19 pandemic. The development of artificial intelligence (AI) is a fast-growing trend in recent years, and it provides new opportunities in the diagnosis, monitoring, prediction, and treatment of infectious diseases. This article studies the key medical image analysis, epidemiological modelling, outbreak prediction, and individualized treatment as the key applications of AI in combating infectious diseases. The restrictions related to the ethical issues, data quality, and incorporation into clinical practice are given special attention. Thus, AI can become an essential instrument that can facilitate the improvement of the healthcare system and minimize global risks related to infectious diseases.
Article Details
References
World Health Organization. (2024). Harnessing artificial intelligence for health. World Health Organization. https://www.who.int/teams/digital-health-and-innovation/harnessing-artificial-intelligence-for-health
Odone, A., Signorelli, C., & Hanvoravongchai, P. (2025). Artificial intelligence and infectious diseases: An evidence review. The Lancet Infectious Diseases. Advance online publication. https://www.thelancet.com/journals/laninf/article/PIIS1473-3099%2825%2900412-8/abstract
Parums, D. V. (2023). Editorial: Infectious disease surveillance using artificial intelligence. Medical Science Monitor, 29, e939212. https://pmc.ncbi.nlm.nih.gov/articles/PMC10240961/ DOI: https://doi.org/10.12659/MSM.941209
Lee, E. H., Lee, J., Kim, H., & Kim, J. (2021). Deep COVID DeteCT: International experience on COVID-19 lung detection using deep learning. NPJ Digital Medicine, 4(1), 125. https://www.nature.com/articles/s41746-020-00369-1 DOI: https://doi.org/10.1038/s41746-020-00369-1
Javaheri, T., Homayounieh, F., & Gholamrezanezhad, A. (2021). CovidCTNet: An open-source deep learning approach to COVID-19 CT classification. Scientific Reports, 11(1), 13642. https://www.nature.com/articles/s41746-021-00399-3 DOI: https://doi.org/10.1038/s41746-021-00399-3
Keshavamurthy, R., Srinivasan, K., & Rao, A. S. (2022). A systematic review of machine learning and deep learning methods for infectious disease prediction. Frontiers in Public Health, 10, 851742. https://pmc.ncbi.nlm.nih.gov/articles/PMC9582566/
Santangelo, O. E., Provenzano, S., & Firenze, A. (2023). Machine learning for infectious disease risk prediction: A systematic review. International Journal of Environmental Research and Public Health, 20(6), 4952. https://www.mdpi.com/2504-4990/5/1/13 DOI: https://doi.org/10.3390/make5010013
Liu, T., Xu, K., & Wang, H. (2022). Deep learning and medical image analysis for COVID-19: Review and recommendations. Annual Review of Biomedical Engineering, 24, 399–421. https://www.annualreviews.org/content/journals/10.1146/annurev-bioeng-110220-012203 DOI: https://doi.org/10.1146/annurev-bioeng-110220-012203
Nishio, M., Noguchi, S., & Matsuo, H. (2022). Automatic classification of COVID-19 pneumonia on CT images with deep learning: A multicenter study. Scientific Reports, 12(1), 1–10. https://www.nature.com/articles/s41598-022-11990-3
Ye, Y., Li, X., & Zhang, Q. (2025). Integrating artificial intelligence with mechanistic epidemiological models: A scoping review. Nature Communications, 16(1), 1123. https://www.nature.com/articles/s41467-024-55461-x DOI: https://doi.org/10.1038/s41467-024-55461-x
Sarantopoulos, A., Koufopoulos, S., & Gourgoulianis, K. I. (2024). Artificial intelligence in infectious disease clinical practice: Opportunities and challenges. Infectious Diseases Reports, 16(2), 133–145. https://www.mdpi.com/2414-6366/9/10/228 DOI: https://doi.org/10.3390/tropicalmed9100228
Akter, S., Shamsuddin, S. M., & Hossain, M. S. (2021). COVID-19 detection using deep learning on chest X-rays. Healthcare, 9(8), 1014. https://pmc.ncbi.nlm.nih.gov/articles/PMC8614951/