Volume 23, Issue 3 (12-2025)
J Sch Public Health Inst Public Health Res. 2025, 23(3): 277-293 |
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Shafaati M, Hassanpour G. Artificial Intelligence in the Control and Management of Human Pathogenic Parasites: A Mini- Review of Current Advances and Future Directions. J Sch Public Health Inst Public Health Res. 2025; 23 (3) :277-293
URL: http://sjsph.tums.ac.ir/article-1-6447-en.html
URL: http://sjsph.tums.ac.ir/article-1-6447-en.html
1- , hassanpour@tums.ac.ir
Abstract: (30 Views)
Background and Aim: Human parasitic diseases, including protozoan, helminthic, and arthropod infections, remain a major threat to public health, and their rapid and accurate diagnosis is of critical importance. Conventional diagnostic approaches, such as microscopy, serological assays, and molecular techniques, are associated with limitations including human error, high costs, and the need for sophisticated equipment. In recent years, advances in artificial intelligence (AI) and deep learning (DL) technologies have created new opportunities to improve the control and management of parasitic diseases.
Materials and Methods: This study was conducted as a mini-review, in which published studies on the applications of artificial intelligence and deep learning in clinical parasitology were reviewed and analyzed.
Results: The findings showed that these technologies have three major applications: prediction of parasitic disease outbreaks, antiparasitic drug discovery, and diagnosis of parasitic infections. Convolutional neural networks (CNNs) and active learning models enable high-precision analysis of microscopic images, epidemic forecasting, identification of therapeutic targets, and optimization of treatment strategies. Nevertheless, challenges such as limited datasets, algorithmic bias, ethical considerations, and inadequate technical infrastructure remain.
Conclusion: Integration of AI with clinical systems, development of comprehensive databases, and application of transfer learning techniques may improve the accuracy, efficiency, and generalizability of AI algorithms. This mini-review summarizes the current status, challenges, and future prospects of AI applications in clinical parasitology and highlights strategies for developing innovative and cost-effective diagnostic and therapeutic tools.
Materials and Methods: This study was conducted as a mini-review, in which published studies on the applications of artificial intelligence and deep learning in clinical parasitology were reviewed and analyzed.
Results: The findings showed that these technologies have three major applications: prediction of parasitic disease outbreaks, antiparasitic drug discovery, and diagnosis of parasitic infections. Convolutional neural networks (CNNs) and active learning models enable high-precision analysis of microscopic images, epidemic forecasting, identification of therapeutic targets, and optimization of treatment strategies. Nevertheless, challenges such as limited datasets, algorithmic bias, ethical considerations, and inadequate technical infrastructure remain.
Conclusion: Integration of AI with clinical systems, development of comprehensive databases, and application of transfer learning techniques may improve the accuracy, efficiency, and generalizability of AI algorithms. This mini-review summarizes the current status, challenges, and future prospects of AI applications in clinical parasitology and highlights strategies for developing innovative and cost-effective diagnostic and therapeutic tools.
Keywords: Artificial Intelligence, Deep Learning, Human Parasitic Diseases, Diagnosis, Prediction, Drug Discovery
Type of Study: Research |
Subject:
Public Health
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