The Next Breakthrough in Maternal Care: AI and the Future of High-Risk Pregnancies

High-risk pregnancies rarely come with one unmistakable warning light; they often unfold as a series of small changes—blood pressure drifting upward, growth measurements starting to slow, or fetal heart rate patterns that look “probably fine” until they are not. That ambiguity is exactly where artificial intelligence can help: not by replacing maternal-fetal medicine teams, but by turning messy, continuous data into earlier signals that something is drifting off course.

In everyday terms, “high-risk pregnancy” can include hypertensive disorders like preeclampsia, diabetes in pregnancy, fetal growth restriction, maternal heart disease, and a history of preterm birth—situations where the margin for delay is small. The practical burden is also heavier—more appointments, more ultrasounds, more monitoring, and often more anxiety—so better prioritization (who needs closer follow-up, and when) can change both outcomes and experience.

Predicting preeclampsia earlier (before symptoms spike)

Preeclampsia is a serious pregnancy complication that typically involves high blood pressure and can affect organs like the kidneys and liver; it can threaten both maternal and fetal health if it progresses. One reason it is so difficult is that early symptoms can be mild or non-specific, meaning risk can be present well before a clear diagnosis is made.

Machine learning models aim to improve prediction by learning patterns across many variables at once—rather than relying only on a short list of traditional risk factors. A population-based cohort study published in Frontiers in Endocrinology reported machine learning performance (using area under the receiver operating characteristic curve, also called “AUC”) of 0.797 for predicting preeclampsia and 0.856 for predicting preterm preeclampsia under the same predictive factors in that dataset.

Another emerging direction uses heart electrical signals captured by an electrocardiogram (often shortened to ECG). In Frontiers in Cardiovascular Medicine, researchers described an artificial intelligence model using electrocardiograms that achieved AUC values of 0.92, 0.89, and 0.90 when tested on electrocardiograms collected 30, 60, and 90 days before preeclampsia diagnosis, respectively, and reported an AUC of 0.98 for early-onset preeclampsia (diagnosed before 34 weeks) in their analysis.

The clinical point is not that an algorithm “diagnoses” preeclampsia. The point is earlier identification of who may benefit from closer surveillance, more frequent blood pressure checks, or earlier specialist involvement—before the situation becomes an emergency.

Fetal monitoring, explained plainly (and why AI helps)

During pregnancy and especially during labor, clinicians often monitor fetal well-being using a test called cardiotocography. Cardiotocography records two signals at the same time: the baby’s heart rate (fetal heart rate) and the parent’s uterine contractions.

The challenge is that cardiotocography interpretation can be subjective, and different clinicians may read the same tracing differently—especially under time pressure or in settings with limited specialist coverage. Google Research described deep learning work showing it is feasible to use cardiotocography to predict fetal hypoxia (low oxygen), and it reported that combining fetal heart rate plus uterine contraction signals produced the highest performance for classifying outcomes based on objective umbilical cord blood pH as well as Apgar score labels.

This distinction matters because umbilical cord blood pH is considered a more objective label than Apgar scoring, which is clinician-assigned and can vary. The most practical near-term role for these models is “decision support”—an added layer that can flag concerning patterns earlier—while the clinician still makes the final judgment call.

Ultrasound AI: scaling expertise beyond big centers

Ultrasound is central to high-risk pregnancy care because decisions often rely on repeat measurements—dating, fetal growth, and anatomy—and trend detection over time. Artificial intelligence features such as automated fetal measurements, real-time anatomy identification, and automated placement of measurement markers (“calipers”) aim to reduce variability and make reliable scanning easier to perform consistently.

A concrete example is the U.S. Food and Drug Administration clearance of the Clarius OB AI fetal biometric measurement tool, which can automatically measure fetal biometry and estimate fetal age, weight, and growth intervals. Reporting on this clearance notes the model was trained using over 30,000 de-identified fetal ultrasound images, with the goal of improving access to prenatal monitoring in areas with limited imaging resources.

For high-risk pregnancies, this kind of “scalable consistency” matters because care decisions—extra surveillance, referral to a tertiary center, or timing of delivery—can hinge on serial measurements and subtle changes over weeks.

Privacy, trust, and governance (HIPAA + PIPEDA)

Artificial intelligence in pregnancy care can involve deeply sensitive information: ultrasound images, fetal monitoring streams, lab results, and sometimes data from remote monitoring tools. In the United States, this raises HIPAA obligations when protected health information is involved, and in Canada comparable privacy expectations must also align with PIPEDA, including limiting collection, protecting data, and using information for clearly stated purposes.

There is also a human-factor risk: “automation bias,” where people may over-trust an algorithm even when clinical context suggests caution. That is why many pregnancy-risk modeling and fetal monitoring efforts emphasize prospective validation and careful integration into clinical workflows—because strong results in retrospective data do not automatically translate into better outcomes in real care.

Wrapping Up

Artificial intelligence is helping high-risk pregnancy care become more proactive by predicting risk earlier (including preeclampsia), improving consistency in cardiotocography interpretation support, and making ultrasound measurements more standardized and accessible. The most realistic near-term value is not a fully automated pregnancy journey, but smarter prioritization—so clinicians can focus time and resources on the patients who need closer surveillance and timely intervention.