AI spots at-risk pregnancies for earlier, more personalized prenatal care

Artificial intelligence (AI) may help identify women and babies at risk of serious health problems earlier in pregnancy, according to a new study analyzing data from more than half a million pregnancies across Sweden, Chile and Singapore. The machine learning models, which used information available during the first 14 weeks of pregnancy, generally outperformed the early risk assessment methods currently used in each setting.

The findings, published in an article in the Journal of Medical Internet Research titled “Machine Learning–Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: Multicenter Model Development Study,” suggest that AI could eventually support more personalized and equitable prenatal care.

The research also found that social and demographic factors were among the most important predictors in some populations, highlighting information that traditional risk assessments may overlook.

In Sweden and Chile, the best-performing machine learning models showed substantially better ability to distinguish between higher- and lower-risk pregnancies than the existing approaches. In Singapore, the improvement was smaller but still statistically significant.

The study also underscores an important challenge: One model did not perform equally well in every population. While the Swedish and Singaporean models showed reasonable agreement between predicted and observed risks, the Chilean model was less well calibrated. This suggests that AI tools for prenatal care may need to be tailored and carefully tested for the populations in which they are used.

The researchers emphasize that such models are not intended to replace health care professionals. Instead, they could serve as decision-support tools, helping clinicians identify pregnancies that may benefit from closer monitoring or earlier intervention.

The study points toward a future in which prenatal risk assessment incorporates not only medical history but also social, demographic and behavioral factors that can shape pregnancy outcomes.

Provided by
JMIR Publications

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Sadie Harley

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Andrew Zinin

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