when ai meets parasitology؛ a brief review on application of ai in parasitology

Ai refers to the simulation of human intelligence in machines designed to perform tasks that typically require human intelligence such as visual perception, speech recognition, decision-making, and language translation. since its early development in the 1950s, ai has been successfully deployed from research outputs and operational efficiencies and improving sustainability, to education, easing workforce strain and improving patient care. ai can provide time savings and insights wherever data exists to train, refine and query the ai models. with ever more complex ‘deep learning’, the non-human ‘brain’ provides an alternative, multi-layered neural network through which data can be analyzed and interpreted. in the realm of parasitology, the accurate identification and classification of parasites in clinical samples are pivotal for effective diagnosis and treatment. furthermore, parasitic infections continue to pose significant health challenges, particularly in regions with limited access to advanced diagnostic tools and treatments. traditional methods in parasitology, while valuable, can be time-consuming, labor-intensive, and may suffer from variability due to subjective interpretation. however, the synergy of ai and with parasitology has opened up novel avenues for enhancing diagnostics, treatment strategies, and research outcomes in the field. in recent years significant advancements in ai capabilities, particularly in areas such as image and speech recognition, natural language processing, and reinforcement learning has usherd us a new era of possibilities in human and veterinary medicine, with parasitology being no exception. all together ai can help in diagnosis of parasites by leveraging algorithms that can analyze complex visual data such as microscopic images, precipitate drug discovery procedures through employing predictive models in virtual screening process, forecast disease outbreaks with precise predictive models and decipher genetic makeup by having the cabability of interpretation of vast datasets.