Mostafa, R., Taha, N., Eissa, F. (2024). Artificial intelligence in Medical Parasitology diagnosis and drug discovery: A systematic review (2014–2024). Parasitologists United Journal, 17(3), 144-155. doi: 10.21608/puj.2024.324262.1271
Reham Mostafa; Noha Taha; Fatma Eissa. "Artificial intelligence in Medical Parasitology diagnosis and drug discovery: A systematic review (2014–2024)". Parasitologists United Journal, 17, 3, 2024, 144-155. doi: 10.21608/puj.2024.324262.1271
Mostafa, R., Taha, N., Eissa, F. (2024). 'Artificial intelligence in Medical Parasitology diagnosis and drug discovery: A systematic review (2014–2024)', Parasitologists United Journal, 17(3), pp. 144-155. doi: 10.21608/puj.2024.324262.1271
Mostafa, R., Taha, N., Eissa, F. Artificial intelligence in Medical Parasitology diagnosis and drug discovery: A systematic review (2014–2024). Parasitologists United Journal, 2024; 17(3): 144-155. doi: 10.21608/puj.2024.324262.1271
Artificial intelligence in Medical Parasitology diagnosis and drug discovery: A systematic review (2014–2024)
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.
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