02683nas a2200313   4500000000100000008004100001260004700042653002800089653003200117653001400149653003100163653003400194653002600228653002400254100001800278700001600296700001600312700001600328700001700344700001700361700001500378700001300393245015400406856006700560300001100627490000600638520171100644022001402355       2026                            d  c06/2026bStrategic Learning Solutions, Inc10aartificial intelligence10aParasitological examination10aDiagnosis10aSoil-transmitted helminths10aclinical laboratory readiness10amedical technologists10atechnology adoption1 aFernandez MJR1 aBalahim FZH1 aCatipay PES1 aDacillo FPY1 aDela Cruz GT1 aEustaquio JR1 aZerrudo JP1 aQuinto L00aArtificial Intelligence Preparedness and Perceived Challenges in Soil-Transmitted Helminth Identification among Medical Technologists in Metro Manila  uhttps://journal.ijhba.com/index.php/ijhba/article/view/164/152  a1 - 150 v23 a<jats:p>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.</jats:p>  a3116-2649