Artificial intelligence in Medical Parasitology diagnosis and drug discovery: A systematic review (2014–2024)

Document Type : Review Article

Authors

Department of Medical Parasitology, Faculty of Medicine, Cairo University, Giza, Egypt

Abstract

Artificial Intelligence (AI) was introduced to the field of Medical Parasitology with many applications including
predicting epidemics, diagnosis, therapeutic approaches, and diseases control. The current systematic
review was conducted to retrieve published articles in the last decade related to AI applications in Medical
Parasitology aiming to provide comprehensive data for more advancement in field diagnosis, and drug
development. The PubMed, Scopus and Web of Science databases were screened systematically for articles
covering AI in Parasitology published from 2014 to 2024, and SWOT analysis was conducted. In diagnosis,
results revealed plenty of AI modalities including mobile applications, machine learning (ML) or deep learning
(DL) based methods, neural network image models, convolutional neural network (CNN), digital microscopy,
helminth egg analysis platform (HEAP), and transfer learning-based techniques. In addition, screening drug
libraries opens new avenues for identification of new drug targets, and drug repurposing or combinations for
better therapeutic regimens. It was concluded that AI modalities can help in making decisions and diagnosing
parasites in various samples. Moreover, AI represents a crucial step for repurposing available drugs, and
discovering drug targets for de novo drug development.

Keywords

Parasitologists United Journal
Volume 17, Issue 3
December 2024
Pages 144-155

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