<![CDATA[Antenatal care (ANC) is an essential component of maternal healthcare because timely identification of pregnancy complications and appropriate follow-up can reduce preventable maternal and perinatal morbidity. Conventional ANC generally applies standardized assessment and follow-up schedules, which may not fully accommodate the dynamic and heterogeneous risk profiles of pregnant women. Artificial intelligence (AI)-based personalized ANC may facilitate individualized risk stratification while simultaneously improving patient engagement and adherence to ANC.This study aimed to evaluate the effectiveness of an AI-based personalized ANC approach in improving early detection of pregnancy complication risks and ANC compliance compared with standard ANC.A quantitative quasi-experimental pretest-posttest control group design was used. The simulated dataset consisted of 120 pregnant women, with 60 participants assigned to an intervention group and 60 to a control group. The intervention consisted of standard ANC supplemented by AI-supported individualized risk screening, personalized health education, and reminder-based follow-up. The control group received standard ANC. The primary outcomes were early detection of pregnancy complication risks and ANC compliance. Categorical outcomes were analyzed using the Chi-square test and effect estimates were expressed as odds ratios (ORs).In the simulated dataset, early detection of pregnancy complication risks was achieved in 48 participants (80.0%) in the intervention group compared with 33 participants (55.0%) in the control group. The intervention was associated with higher odds of early detection (OR = 3.27). ANC compliance was observed in 51 participants (85.0%) in the intervention group and 36 participants (60.0%) in the control group, corresponding to an OR of 3.78. Both outcomes demonstrated statistically significant between-group differences in the simulated analysis. The simulated findings suggest that AI-based personalized ANC may improve early identification of pregnancy complication risks and ANC compliance. AI may therefore serve as a complementary decision-support and patient-engagement technology within maternal healthcare. However, these numerical findings are synthetic and must not be interpreted or submitted as empirical findings from actual participants.]]>
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