Artificial intelligence in parasitic disease control: A paradigm shift in health care

• Published in: Tropical Parasitology Vol. 14; no. 1; pp. 2 - 7
• Main Authors: Parija, Subhash Chandra, Poddar, Abhijit
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer - Medknow 2024; Medknow Publications & Media Pvt. Ltd
• Edition: 2
• Subjects: Artificial intelligence; At risk populations; Disease control; Disease spread; Disease transmission; Drug development; Drug discovery; Environmental factors; Epidemics; Epidemiology; Health care; Health promotion; Leishmaniasis; Malaria; Outbreaks; Parasites; Parasitic diseases; Presidential Oration; Public health; Resource allocation; Therapeutic targets; Tropical diseases; Trypanosomiasis; Vector-borne diseases; parasitic diseases; machine learning; Artificial intelligence; health care
• ISSN: 2229-5070; 2229-7758
• DOI: 10.4103/tp.tp_66_23

Parasitic diseases, including malaria, leishmaniasis, and trypanosomiasis, continue to plague populations worldwide, particularly in resource-limited settings and disproportionately affecting vulnerable populations. It has limited the use of conventional health-care delivery and disease control approaches and necessitated exploring innovative strategies. In this direction, artificial intelligence (AI) has emerged as a transformative tool with immense promise in parasitic disease control, offering the potential for enhanced diagnostics, precision drug discovery, predictive modeling, and personalized treatment. Predictive AI algorithms have assisted in understanding parasite transmission patterns and outbreaks by analyzing vast amounts of epidemiological data, environmental factors, and population demographics. This has strengthened public health interventions, resource allocation, and outbreak preparedness strategies, enabling proactive measures to mitigate disease spread. In diagnostics, AI-enabled accurate and rapid identification of parasites by analyzing microscopic images. This capability is particularly valuable in remote regions with limited access to diagnostic facilities. AI-driven computational methods have also assisted in drug discovery for parasitic diseases by identifying novel drug targets and predicting the efficacy and safety of potential drug candidates. This approach has streamlined drug development, leading to more effective and targeted therapies. This article reviews these current developments and their transformative impacts on the health-care sector. It also assessed the hurdles that require attention before these transformations can be realized in real-life scenarios.