<![CDATA[Background: Preeclampsia remains a leading cause of maternal mortality worldwide, yet awareness and early detection remain limited in low- and middle-income countries. While artificial intelligence (AI)-based applications have been increasingly utilized in hospital settings, their adoption in Indonesian primary care remains minimal. This study aimed to evaluate the effectiveness of an AI-based mobile health application (PE-CARE) in improving maternal knowledge on early detection of preeclampsia. Methods: A quasi-experimental pretest–posttest control group design was conducted at Puskesmas Parongpong, West Bandung Regency, from February to March 2025. A total of 100 pregnant women (≤20 weeks gestation) were recruited using purposive sampling and assigned equally to the intervention (n=50) and control (n=50) groups. The intervention group used the PE-CARE application for 14 days, while the control group received conventional health education. Knowledge was assessed using a validated 15-item questionnaire. Data were analyzed using paired and independent t-tests, complemented by effect size (Cohen’s d) and 95% confidence intervals. Results: Knowledge scores improved significantly in both groups, with a larger gain in the intervention group (mean difference 28.1; Cohen’s d=3.79, 95% CI 25.7–30.5, p<0.001) compared to the control group (mean difference 11.5; Cohen’s d=1.56, 95% CI 9.3–13.7, p<0.001). Between-group comparison of posttest scores confirmed a significant effect favoring the intervention (mean difference 21.3; Cohen’s d=4.05, 95% CI 18.8–24.8, p<0.001). Conclusion: The PE-CARE application was effective in improving maternal knowledge of preeclampsia in a primary care setting. While these findings demonstrate the potential of AI-based mobile health tools to complement antenatal education, further research is needed to evaluate long-term behavioral and clinical outcomes as well as implementation feasibility in diverse primary care contexts.]]>
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