(untitled) (The Review of Diabetic Studies, 2025)
Food poisoning is a significant menace to global population health, yet there is a lack of rapid, proactive, and efficient detection methods. Theoretical breakthroughs have occurred in biosensors, molecular assays, and artificial intelligence (AI), there is little empirical data on the quantitative effects of technological adoption on detection accuracy, speed, and reliability in institutional settings. This paper sought to establish how the use of modern diagnostic technology has impacted the identification of food-borne pathogens and also provided the factors that have influenced the use of contemporary diagnostic technology in both laboratories and regulatory settings. An approach was used that employed a descriptive-correlational research design, involving 150 professionals selected from 25 government, private, and academic institutions. The structured questionnaires and laboratory observation checklists were used to collect the data, and analyzed with SPSS version 26 using descriptive statistics, Pearson correlation, multiple regression, and one-way ANOVA to analyze the data. Findings indicated that average use of technology was high (M = 6.51, 2.03), and the mean detection accuracy was 85.88, 7.32, and the reliability index was 0.80, 0.08. The use of technology also had a positive association with accuracy (r = 0.708, p < 0.001), reliability (r = 0.780, p < 0.001), and a negative association with the detection time (r = -0.778, p = 0.001). The regression analysis proved technology as a powerful predictor of the accuracy ( 2.55, p < 0.001, R 2 = 0.52). The results prove that modern technologies play a significant role in improving the performance of diagnoses among the types of institutions, which should be introduced into the food safety system to ensure the rapid, reliable, and proactive prevention of diseases.