{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:40:14Z","timestamp":1789105214469,"version":"build-2803163510"},"reference-count":33,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.4103\/tp.tp_66_23","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T07:00:19Z","timestamp":1708326019000},"page":"2-7","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":29,"title":["Artificial intelligence in parasitic disease control: A paradigm shift in health care"],"prefix":"10.4103","volume":"14","author":[{"given":"Subhash Chandra","family":"Parija","sequence":"first","affiliation":[{"name":"Professor Emeritus, National Academy of Medical Sciences, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhijit","family":"Poddar","sequence":"additional","affiliation":[{"name":"Mahatma Gandhi Medical Advanced Research Institute, Sri Balaji Vidyapeeth (Deemed to be University), Puducherry, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"key":"R1-20240909","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare:Past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc Neurol"},{"key":"R4-20240909","doi-asserted-by":"crossref","first-page":"549","DOI":"10.3389\/fimmu.2019.00549","article-title":"Infectious disease threats in the twenty-first century: Strengthening the global response","volume":"10","author":"Bloom","year":"2019","journal-title":"Front Immunol"},{"key":"R5-20240909","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1086\/669114","article-title":"Governing future potential biothreats: Toward an anthropology of uncertainty","volume":"54","author":"Samimian-Darash","year":"2013","journal-title":"Curr Anthropol"},{"key":"R6-20240909","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1016\/S0020-7519(00)00141-7","article-title":"Effects of environmental change on emerging parasitic diseases","volume":"30","author":"Patz","year":"2000","journal-title":"Int J Parasitol"},{"key":"R7-20240909","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1038\/s41467-019-08616-0","article-title":"On the predictability of infectious disease outbreaks","volume":"10","author":"Scarpino","year":"2019","journal-title":"Nat Commun"},{"key":"R8-20240909","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S0065-308X(00)47013-2","article-title":"Forecasting disease risk for increased epidemic preparedness in public health","volume":"47","author":"Myers","year":"2000","journal-title":"Adv Parasitol"},{"key":"R9-20240909","doi-asserted-by":"crossref","first-page":"115604","DOI":"10.1016\/j.eswa.2021.115604","article-title":"Forecasting influenza epidemics in Hong Kong using Google search queries data: A new integrated approach","volume":"185","author":"Liu","year":"2021","journal-title":"Expert Syst Appl"},{"key":"R10-20240909","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3390\/tropicalmed3010005","article-title":"Application of artificial neural networks for dengue fever outbreak predictions in the northwest coast of Yucatan, Mexico and San Juan, Puerto Rico","volume":"3","author":"Laureano-Rosario","year":"2018","journal-title":"Trop Med Infect Dis"},{"key":"R11-20240909","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1016\/S1473-3099(16)30270-5","article-title":"Potential for Zika virus introduction and transmission in resource-limited countries in Africa and the Asia-Pacific region: A modelling study","volume":"16","author":"Bogoch","year":"2016","journal-title":"Lancet Infect Dis"},{"key":"R12-20240909","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1109\/ICSTCEE49637.2020.9277286","article-title":"Vector Borne Disease Outbreak Prediction by Machine Learning","author":"Raizada","year":"2020","journal-title":"2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE). IEEE"},{"key":"R13-20240909","first-page":"1087","article-title":"A review of predictive models on diagnosis and treatment of malaria fever","volume":"4","author":"Oguntimilehin","year":"2015","journal-title":"Int J Comput Sci Mob Comput"},{"key":"R14-20240909","first-page":"102854","article-title":"Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping","volume":"112","author":"Shabanpour","year":"2022","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"R15-20240909","doi-asserted-by":"crossref","first-page":"939","DOI":"10.4155\/fmc-2019-0307","article-title":"An overview of drug discovery and development","volume":"12","author":"Berdigaliyev","year":"2020","journal-title":"Future Med Chem"},{"key":"R16-20240909","doi-asserted-by":"crossref","first-page":"3049","DOI":"10.1016\/j.apsb.2022.02.002","article-title":"Why 90% of clinical drug development fails and how to improve it?","volume":"12","author":"Sun","year":"2022","journal-title":"Acta Pharm Sin B"},{"key":"R17-20240909","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1038\/nrd4609","article-title":"An analysis of the attrition of drug candidates from four major pharmaceutical companies","volume":"14","author":"Waring","year":"2015","journal-title":"Nat Rev Drug Discov"},{"key":"R18-20240909","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S0167-6296(02)00126-1","article-title":"The price of innovation: New estimates of drug development costs","volume":"22","author":"DiMasi","year":"2003","journal-title":"J Health Econ"},{"key":"R19-20240909","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1007\/s11030-021-10217-3","article-title":"Artificial intelligence to deep learning: Machine intelligence approach for drug discovery","volume":"25","author":"Gupta","year":"2021","journal-title":"Mol Divers"},{"key":"R20-20240909","doi-asserted-by":"crossref","first-page":"e667","DOI":"10.1016\/S2589-7500(20)30192-8","article-title":"Artificial intelligence in COVID-19 drug repurposing","volume":"2","author":"Zhou","year":"2020","journal-title":"Lancet Digit Health"},{"key":"R21-20240909","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/03602532.2020.1726944","article-title":"Entering the era of computationally driven drug development","volume":"52","author":"Maharao","year":"2020","journal-title":"Drug Metab Rev"},{"key":"R22-20240909","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1002\/cmdc.202000685","article-title":"Artificial intelligence applied to the rapid identification of new antimalarial candidates with dual-stage activity","volume":"16","author":"Lima","year":"2021","journal-title":"ChemMedChem"},{"key":"R23-20240909","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.3389\/fphar.2019.01526","article-title":"DeepMalaria: Artificial intelligence driven discovery of potent antiplasmodials","volume":"10","author":"Keshavarzi","year":"2019","journal-title":"Front Pharmacol"},{"key":"R24-20240909","doi-asserted-by":"crossref","first-page":"4450","DOI":"10.1021\/acs.jcim.9b00375","article-title":"All-Assay-Max2 pQSAR: Activity predictions as accurate as four-concentration IC(50)s for 8558 Novartis assays","volume":"59","author":"Martin","year":"2019","journal-title":"J Chem Inf Model"},{"key":"R25-20240909","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.pt.2023.01.010","article-title":"Drug discovery for parasitic diseases: Powered by technology, enabled by pharmacology, informed by clinical science","volume":"39","author":"Rao","year":"2023","journal-title":"Trends Parasitol"},{"key":"R26-20240909","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.ijpharm.2008.09.036","article-title":"Development of novel formulations for Chagas'disease: Optimization of benznidazole chitosan microparticles based on artificial neural networks","volume":"367","author":"Leonardi","year":"2009","journal-title":"Int J Pharm"},{"key":"R27-20240909","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/s41579-022-00777-y","article-title":"Anti-trypanosomatid drug discovery: Progress and challenges","volume":"21","author":"De","year":"2023","journal-title":"Nat Rev Microbiol"},{"key":"R28-20240909","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1042\/BST20220618","article-title":"Targeting trypanosomes: How chemogenomics and artificial intelligence can guide drug discovery","volume":"51","author":"Ur\u00e1n","year":"2023","journal-title":"Biochem Soc Trans"},{"key":"R29-20240909","doi-asserted-by":"crossref","first-page":"614073","DOI":"10.3389\/fchem.2021.614073","article-title":"Use of artificial intelligence and machine learning for discovery of drugs for neglected tropical diseases","volume":"9","author":"Winkler","year":"2021","journal-title":"Front Chem"},{"key":"R30-20240909","doi-asserted-by":"crossref","first-page":"20141289","DOI":"10.1098\/rsif.2014.1289","article-title":"Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases","volume":"12","author":"Williams","year":"2015","journal-title":"J R Soc Interface"},{"key":"R31-20240909","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4103\/tp.tp_32_22","article-title":"Climate adaptation impacting parasitic infection","volume":"12","author":"Parija","year":"2022","journal-title":"Trop Parasitol"},{"key":"R32-20240909","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4103\/tp.tp_12_23","article-title":"Deep tech innovation for parasite diagnosis: New dimensions and opportunities","volume":"13","author":"Parija","year":"2023","journal-title":"Trop Parasitol"},{"key":"R33-20240909","doi-asserted-by":"crossref","first-page":"100806","DOI":"10.1016\/j.patter.2023.100806","article-title":"AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images","volume":"4","author":"Liu","year":"2023","journal-title":"Patterns (N Y)"},{"key":"R35-20240909","doi-asserted-by":"crossref","first-page":"3344","DOI":"10.1109\/EMBC46164.2021.9630868","article-title":"Combining collective and artificial intelligence for global health diseases diagnosis using crowdsourced annotated medical images","author":"Lin","year":"2021","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc 2021"},{"key":"R37-20240909","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1177\/08404704221125368","article-title":"Equity within AI systems: What can health leaders expect?","volume":"36","author":"Gurevich","year":"2023","journal-title":"Healthc Manage Forum"}],"container-title":["Tropical Parasitology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/tp.tp_66_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T18:22:12Z","timestamp":1787768532000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/tp.tp_66_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.4103\/tp.tp_66_23","relation":{},"ISSN":["2229-5070","2229-7758"],"issn-type":[{"value":"2229-5070","type":"print"},{"value":"2229-7758","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"2023-12-18","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-19","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}