Artificial Intelligence Preparedness and Perceived Challenges in Soil-Transmitted Helminth Identification among Medical Technologists in Metro Manila
DOI:
https://doi.org/10.65166/z96vbj06Keywords:
artificial intelligence, clinical laboratory readiness, medical technologists, parasitological diagnosis, soil-transmitted helminths, technology adoptionAbstract
Artificial intelligence-assisted microscopy may improve the efficiency and consistency of soil-transmitted helminth identification, but its implementation depends on workforce preparedness and institutional capacity. This study assessed preparedness and perceived challenges related to artificial intelligence integration among registered medical technologists working in secondary and tertiary hospitals in Metro Manila. A cross-sectional descriptive-comparative survey was conducted among 100 respondents using a validated researcher-developed questionnaire. Frequencies, percentages, means, and standard deviations were used for descriptive analysis. Kruskal-Wallis H and Mann-Whitney U tests examined differences according to age, professional experience, and the availability of institutional artificial intelligence seminars or workshops, with a Bonferroni-adjusted alpha of .017. Overall preparedness was moderate (M = 2.61, SD = 0.51). Willingness to learn artificial intelligence-based diagnostic tools was high (M = 3.05), whereas perceived laboratory readiness was low (M = 2.15). Perceived challenges were high overall (M = 3.22, SD = 0.39). Financial constraints were rated very high (M = 3.44), while inadequate infrastructure (M = 3.10) and limited educational opportunities (M = 3.06) were rated high. No statistically significant differences were detected across the tested respondent characteristics. The findings indicate that medical technologists are receptive to artificial intelligence, but institutional financing, structured training, reliable infrastructure, and professional oversight are necessary before AI-assisted parasitology can be implemented responsibly.
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Atento, R. G. O., Quinto, L. F., & Espelita, C. A. M. H. (2025). Bridging global health workforce gaps 2050: A multilevel analysis of global demand, Philippine supply fragilities, and competency alignment. International Journal of Health and Business Analytics, 1(2), 1–30. https://doi.org/10.65166/kgbpey79
Atento, R. G., Quinto, L., Espelita, C. A. M., & Castaneda, C. (2025). Integrating business and health analytics: A conceptual framework for dual outcomes in healthcare. International Journal of Health & Business Analytics, 1(1). https://doi.org/10.65166/04pdc866
Atento, R. G. O., Quinto, L. F., Espelita, C. A. M., & San Juan, F. M. (2025). Narrative health analytics: Integrating empathy, data, and ethics in patient-centered healthcare. International Journal of Health and Business Analytics, 1(2), 1–33. https://doi.org/10.65166/yxgx8e59
Bendal, A., Sabasa, S. A., Espelita, C. A. M. H., & Atento, R. G. O. (2026). Artificial intelligence as disruptive technology in accounting: A qualitative study of practitioner perceptions on automation, judgment, and decision support. Journal of Enterprise Strategy & Management Innovation, 1(1). https://doi.org/10.65166/0sdayg70
Cucio, M., & Hennig, T. (2025). Artificial intelligence and the Philippine labor market: Mapping occupational exposure and complementarity (IMF Working Paper No. 2025/043). International Monetary Fund. https://doi.org/10.5089/9798229001977.001
Cure-Bolt, N., Perez, F., Broadfield, L. A., Levecke, B., Hu, P., Oleynick, J., Beltrán, M., Ward, P., & Stuyver, L. J. (2024). Artificial intelligence-based digital pathology for the detection and quantification of soil-transmitted helminths eggs. PLOS Neglected Tropical Diseases, 18(9), e0012492. https://doi.org/10.1371/journal.pntd.0012492
De Niz, M., Silva Pereira, S., Kirchenbuechler, D., Lemgruber, L., & Arvanitis, C. (2026). Artificial intelligence-powered microscopy: Transforming the landscape of parasitology. Journal of Microscopy, 301(2), 280-329. https://doi.org/10.1111/jmi.13433
Grau-Pujol, B., Martí-Soler, H., Escola, V., Demontis, M., Jamine, J. C., Gandasegui, J., Muchisse, O., Cambra-Pellejà, M., Cossa, A., Martínez-Valladares, M., Sacoor, C., Van Lieshout, L., Cano, J., Giorgi, E., & Muñoz, J. (2021). Towards soil-transmitted helminths transmission interruption: The impact of diagnostic tools on infection prediction in a low-intensity setting in Southern Mozambique. PLOS Neglected Tropical Diseases, 15(10), e0009803. https://doi.org/10.1371/journal.pntd.0009803
He, W., Zhu, H., Geng, J., Hu, X., Li, Y., Shi, H., Wang, Y., Zhu, D., Wang, H., Xie, L., Yang, H., & Li, J. (2024). Recognition of parasitic helminth eggs via a deep learning-based platform. Frontiers in Microbiology, 15, 1485001. https://doi.org/10.3389/fmicb.2024.1485001
Kenneth, M. H., Tibakanya, J., & Nakasi, R. (2024). AI methods and algorithms for diagnosis of intestinal parasites: Applications, challenges and future opportunities. East African Journal of Information Technology, 7(1), 366-379. https://doi.org/10.37284/eajit.7.1.2282
Kumar, S., Arif, T., Alotaibi, A. S., Malik, M. B., & Manhas, J. (2023). Advances towards automatic detection and classification of parasites microscopic images using deep convolutional neural network: Methods, models and research directions. Archives of Computational Methods in Engineering, 30(3), 2013-2039. https://doi.org/10.1007/s11831-022-09858-w
Lee, C. C., Huang, P. J., Yeh, P. H., Li, P. P., Chiu, C. H., Cheng, W. H., & Tang, P. (2022). Helminth egg analysis platform (HEAP): An opened platform for microscopic helminth egg identification and quantification based on the integration of deep learning architectures. Journal of Microbiology, Immunology and Infection, 55(3), 395-404. https://doi.org/10.1016/j.jmii.2021.07.014
Lundin, J., Suutala, A., Holmström, O., Henriksson, S., Valkamo, S., Kaingu, H., Kinyua, F., Muinde, M., Lundin, M., Diwan, V., Mårtensson, A., & Linder, N. (2024). Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited setting. PLOS Neglected Tropical Diseases, 18(4), e0012041. https://doi.org/10.1371/journal.pntd.0012041
Mationg, M. L. S., Tallo, V. L., Williams, G. M., Gordon, C. A., Clements, A. C. A., McManus, D. P., & Gray, D. J. (2021). The control of soil-transmitted helminthiases in the Philippines: The story continues. Infectious Diseases of Poverty, 10, Article 85. https://doi.org/10.1186/s40249-021-00870-z
Meulah, B., Bengtson, M., Van Lieshout, L., Hokke, C. H., Kreidenweiss, A., Diehl, J.-C., Adegnika, A. A., & Agbana, T. E. (2023). A review on innovative optical devices for the diagnosis of human soil-transmitted helminthiasis and schistosomiasis: From research and development to commercialization. Parasitology, 150(2), 137-149. https://doi.org/10.1017/S0031182022001664
Microsoft Philippines Communications Team. (2024, May 23). Microsoft and LinkedIn release 2024 Work Trend Index on AI use at work in the Philippines. Microsoft News Center Philippines. https://news.microsoft.com/en-ph/2024/05/23/microsoft-and-linkedin-release-2024-work-trend-index-on-ai-use-at-work-in-the-philippines/
Miswan, N., Singham, G. V., & Othman, N. (2022). Advantages and limitations of microscopy and molecular detections for diagnosis of soil-transmitted helminths: An overview. Helminthologia, 59(4), 321-340. https://doi.org/10.2478/helm-2022-0034
Oduoye, M. O., Fatima, E., Muzammil, M. A., Dave, T., Irfan, H., Fariha, F. N. U., Marbell, A., Ubechu, S. C., Scott, G. Y., & Elebesunu, E. E. (2024). Impacts of the advancement in artificial intelligence on laboratory medicine in low- and middle-income countries: Challenges and recommendations-A literature review. Health Science Reports, 7(1), e1794. https://doi.org/10.1002/hsr2.1794
Parija, S. C., & Poddar, A. (2024). Artificial intelligence in parasitic disease control: A paradigm shift in health care. Tropical Parasitology, 14(1), 2-7. https://doi.org/10.4103/tp.tp_66_23
Penpong, N., Wanna, Y., Kamjanlard, C., Techasen, A., & Intharah, T. (2024). Attacking the out-of-domain problem of a parasite egg detection in-the-wild. Heliyon, 10(4), e26153. https://doi.org/10.1016/j.heliyon.2024.e26153
Rao, L., Tian, Y., & Atento, R. G. O. (2025). Adoption and perceived effectiveness of AI in education: Personalization, outcomes, and equity. International Journal of Health & Business Analytics, 1(1). https://doi.org/10.65166/qgq89291
Sarmiento, R. F. R., Overgaard, S. M., Gai, C., Overgaard, J. D., & Ohde, J. W. (2025). Guiding responsible AI in healthcare in the Philippines. npj Digital Medicine, 8, Article 338. https://doi.org/10.1038/s41746-025-01755-3
Somono, L., & Generoso, M. J. I. A., II. (2026). IT infrastructure and data privacy practices in manufacturing companies. Journal of Enterprise Strategy & Management Innovation, 1(1). https://doi.org/10.65166/dyzqhe81
Tursynbek, A., Zhaksylykova, D., Cruz, J. P., & Balay-Odao, E. M. (2024). Perspectives of patients regarding artificial intelligence and its application in healthcare: A qualitative study. Journal of Clinical Nursing. Advance online publication. https://doi.org/10.1111/jocn.17584
Ward, P., Dahlberg, P., Lagatie, O., Larsson, J., Tynong, A., Vlaminck, J., Zumpe, M., Ame, S., Ayana, M., Khieu, V., Mekonnen, Z., Odiere, M., Yohannes, T., Van Hoecke, S., Levecke, B., & Stuyver, L. J. (2022). Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears. PLOS Neglected Tropical Diseases, 16(6), e0010500. https://doi.org/10.1371/journal.pntd.0010500
World Health Organization. (2023, January 18). Soil-transmitted helminth infections. https://www.who.int/news-room/fact-sheets/detail/soil-transmitted-helminth-infections
Xu, W., Zhai, Q., Liu, J., Xu, X., & Hua, J. (2024). A lightweight deep-learning model for parasite egg detection in microscopy images. Parasites & Vectors, 17, Article 454. https://doi.org/10.1186/s13071-024-06503-2
Zhu, H., Li, Y., Zhu, D., Wang, Y., Zhang, J., Chen, S., Ma, X., Wang, H., Li, H., & Li, J. (2024). Establishment and application of an artificial intelligence-assisted platform for detection of parasite eggs [Article in Chinese]. Chinese Journal of Schistosomiasis Control, 36(6), 643-648. https://doi.org/10.16250/j.32.1374.2024094
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Copyright (c) 2026 Marielle Jane R. Fernandez, Fatima Zahra H. Balahim, Princess Emerald S. Catipay, Francis Philip Y. Dacillo, Geraldine T. Dela Cruz, Jheammy R. Eustaquio, Jeaneil P. Zerrudo, Dr. Leah F. Quinto (Author)
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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