From the experience of using digitalization, machine learning and artificial intelligence in the development of new methods in modern parasitology

The article provides an overview of Russian and foreign literature on modern methods in parasitology. The methods of modern digitalization, machine learning and artificial intelligence are shown as the most promising areas for the development of scientific knowledge. It is determined that the creation of new breakthrough technologies – computer, cloud and databases significantly simplifies the conduct of parasitological research and allows a modern scientist to reach a new, higher-quality level of modern knowledge. The integration of digital solutions based on information and communication technologies into the work of parasitology specialists is an important area for increasing the productivity of modern science. This approach promotes a shift away from classical methods of parasitological research towards innovative developments using intelligent interfaces. Gradually, digital tools are becoming an integral part of the daily practice of parasitology, bringing research to a higher technological level. The analysis of the specifics of the introduction of IT technologies in this scientific field suggests that their practical application contributes to a deeper understanding of biological processes, helps to overcome difficulties in studying parasitic systems, and also reveals the potential of augmented reality technologies for more effective perception and analysis of scientific information in various research areas.

1. Gavrilyuk E.S., Izotova A.G. The main directions and factors of digital transformation of the science and education sector //Scientific Journal of the National Research University of ITMO. The series "Economics and Environmental Management". – 2021. – №. 1 — in Russian.

2. Mironenko V.M. Information system of epizootological monitoring and forecasting of parasitoses //Agrarian science-agriculture. 2015. – pp. 273-274 — in Russian.

3. Elmehankar M.S., Etewa S.E., Metwally A.S. Artificial Intelligence Applications in Medical Parasitology: A Comprehensive Review //Egyptian Academic Journal of Biological Sciences, E. Medical Entomology & Parasitology. – 2023. – Т. 15. – №. 2. – С. 35-43.

4. Gbonhinbor J., Abah A. E., Awi-Waadu G. D. B. Geo-Mapping of Intestinal Parasitic Infection in a Southern Community in Nigeria // Ethiopian Journal of Health Sciences. – 2024. – Т. 34. – №. 3.

5. Carneiro H. A., Mylonakis E. Google trends: a web-based tool for real-time surveillance of disease outbreaks //Clinical infectious diseases. – 2019. – Т. 49. – №. 10. – С. 1557-1564.

6. Poulin R. et al. iParasitology: mining the internet to test parasitological hypotheses //Trends in Parasitology. – 2021. – Т. 37. – №. 4. – С. 267-272.

7. Cringoli G. et al. The Kubic FLOTAC microscope (KFM): a new compact digital microscope for helminth egg counts //Parasitology. – 2021. – Т. 148. – №. 4. – С. 427-434.

8. Galindez J.A.M., Arteaga L.R.V., Cañas R.V. ParasitoBank dataset for diagnosing intestinal parasitism: Helminths and protozoa in coprological samples //Data in Brief. – 2025. – Т. 58. – С. 111279.

9. Mironenko V.M., Korchevskaya E.A., Mayevskaya S.S. Development of artificial intelligence for the diagnosis of parasitosis based on the Hamming neural network // Veterinary biotechnology. – 2013. – № 22(22). – Pp. 355-362. — in Russian.

10. Yan R. et al. A preliminary study on the mixed teaching of human parasitology based on MOOC resources and the experimental teaching digital platform //Zhongguo xue xi Chong Bing Fang zhi za zhi= Chinese Journal of Schistosomiasis Control. – 2021. – Т. 33. – №. 1. – С. 74-78

11. López-Arencibia A., Expósito-Izquierdo C. Artificial intelligence as a didactic tool in the teaching of infectious diseases // INTED 2024 Proceedings. – IATED, 2024. – С. 7042-7050.

12. Aslanova M.M. et al. Creation of a virtual museum of parasitic pathogens to ensure highly effective diagnosis of parasitosis // Healthcare of the Russian Federation. – 2021. – Vol. 65. – No. 1. – pp. 74-77 — in Russian.