Applications of Artificial Intelligence in Parasitology: A holistic perspective from diagnosis to therapeutics.
Authors
Affiliations (2)
Affiliations (2)
- Department of Parasitology, Faculty of Veterinary Medicine, University of Firat, 23119, Elazig, Türkiye.
- Department of Parasitology, Faculty of Veterinary Medicine, University of Firat, 23119, Elazig, Türkiye. Electronic address: [email protected].
Abstract
Artificial intelligence (AI) is increasingly being investigated to address challenges in parasite detection, disease surveillance, and therapeutic development. This narrative review examines current and emerging AI applications in parasitology across diagnostics, drug discovery, genomics, epidemiology, education, and public health decision-making, with particular emphasis on diagnostic applications. Machine learning and deep learning methods support automated parasite detection, classification, and quantification in microscopic images, alongside medical image analysis and arthropod identification. Applications are discussed for malaria, leishmaniasis, schistosomiasis, cystic echinococcosis, soil-transmitted helminth infections, and intestinal protozoal infections. Beyond diagnosis, AI-assisted approaches support compound screening, drug repurposing, toxicity prediction, and prioritization of potential therapeutic targets. Genomic and transcriptomic analyses also offer opportunities to investigate parasite biology and predict drug resistance. Surveillance models may inform outbreak forecasting and targeted control, although their operational value requires evaluation in the intended settings. Educational applications remain emerging, and the benefits of digital learning should be distinguished from those attributable specifically to AI. Across these domains, performance on curated datasets does not establish clinical utility or generalizability. Major barriers include limited representative datasets, insufficient external and prospective validation, infrastructure constraints, and concerns regarding privacy, bias, and accountability. Future progress requires reproducible evaluation, experimental validation of therapeutic candidates, and assessment of clinical and public health benefits. AI is therefore best positioned as a complementary tool that supports parasitological expertise, with responsible implementation guided by transparency, equitable access, and human oversight.