Can AI Predict Pregnancy Complications Before Symptoms Begin?
Pregnancy care has always involved prediction. Clinicians use medical history, blood pressure, blood tests, ultrasound findings and other information to identify pregnancies at increased risk of complications. Artificial intelligence promises to analyse more of this information simultaneously—and potentially identify important patterns earlier.
AI can already predict some pregnancy complications with promising accuracy in research settings, particularly pre-eclampsia and gestational diabetes. But prediction is not diagnosis, and strong performance in one study does not guarantee that an algorithm will work equally well across hospitals or populations. AI currently has greater potential as clinical decision support than as a replacement for established antenatal care. (PubMed)
What Does AI Actually Predict?
Most pregnancy prediction systems do not “see the future.” They calculate probability.
Machine-learning models can be trained using data from previous pregnancies, including maternal age, blood pressure, medical history, laboratory results, ultrasound measurements, medications, previous pregnancy outcomes and biomarkers.
The algorithm identifies statistical relationships between those inputs and later outcomes. When information from another pregnancy is analysed, it estimates whether that pregnancy resembles pregnancies in which a particular complication occurred.
The important clinical question is not simply whether AI can generate a risk score.
It is whether that prediction is sufficiently accurate, early and clinically useful to improve care.
Which Pregnancy Complications Are Being Studied?
Research is particularly active in pre-eclampsia, gestational diabetes, preterm birth and fetal growth complications.
These are attractive targets because identifying increased risk before symptoms or complications develop could potentially change surveillance, testing or preventive care.
A 2025 systematic review examining AI for early detection of pre-eclampsia and gestational diabetes found that models frequently reported area-under-the-curve values above 0.85, indicating potentially strong discrimination between higher- and lower-risk pregnancies.
But only nine studies met the review’s eligibility criteria, and the researchers identified limited external validation and substantial differences between studies as barriers to clinical translation. (PubMed)
Promising performance is therefore not the same as proven routine clinical effectiveness.
Why Is Pre-Eclampsia a Major Target?
Pre-eclampsia is a pregnancy complication involving high blood pressure and potentially dysfunction affecting organs and placental function.
Risk assessment already uses established clinical factors and, in some settings, biomarkers.
Machine learning could potentially analyse these factors together more effectively.
A 2025 systematic review of 11 studies covering 116,253 pregnancies found that machine-learning models predicting pre-eclampsia reported AUC values ranging from 0.84 to 0.973. Important predictors included mean arterial pressure, previous pre-eclampsia and biomarkers including placental growth factor and pregnancy-associated plasma protein A. (PubMed)
The limitation was significant: only three studies conducted external validation.
An algorithm can perform extremely well on data resembling the information used to develop it and perform less effectively when introduced to a different population.
Can AI Predict Gestational Diabetes Earlier?
Gestational diabetes is another major research target because diagnosis usually occurs after pregnancy is already well established.
Researchers have investigated whether information available earlier—including age, body mass index, family history, previous pregnancy history and blood results—can identify women at increased risk before conventional testing.
The 2025 systematic review of pre-eclampsia and gestational diabetes found examples of strong performance, including an XGBoost gestational-diabetes model with an AUC of 0.946. (PubMed)
That does not mean an AI test can replace routine gestational-diabetes screening.
A more realistic clinical role is earlier risk stratification: identifying pregnancies that may warrant additional attention while established diagnostic pathways remain in place.
Why Can an Accurate AI Model Still Fail?
Accuracy statistics require context.
A model developed using records from one hospital network may learn patterns specific to its patients, laboratory systems, clinical practices and electronic records.
Move that algorithm elsewhere and its performance can change.
A 2026 meta-analysis illustrates the problem. Across 26 studies and 31 machine-learning models predicting pre-eclampsia, the pooled AUC was 0.91. But heterogeneity exceeded 99%, and the prediction interval for sensitivity ranged from 0.32 to 0.96. (PubMed)
In other words, impressive average performance concealed considerable uncertainty about how effectively models might identify cases in future clinical settings.
Independent external validation is therefore critical.
Continue Learning on Sistapedia
Explore trusted information across fertility, pregnancy, birth, postpartum recovery, breastfeeding, baby and newborn health, hormonal health, menopause and reproductive medicine.
Women come to Sistapedia to learn. Professionals come to educate.
Could AI Make Pregnancy Care More Personalised?
Potentially.
Traditional risk assessment often groups people into broad categories. Machine learning may eventually allow risk estimates to incorporate more variables and update as new information becomes available.
But a more precise prediction is useful only when it leads to an evidence-based clinical action.
An algorithm that changes someone’s calculated risk from 10% to 17% is not automatically valuable. Clinicians need to know whether that difference should alter monitoring, testing or treatment—and whether doing so improves outcomes.
Clinical usefulness therefore requires more than prediction accuracy.
What About Wearables and At-Home Devices?
Pregnancy increasingly generates health information outside hospitals.
Wearables can measure heart rate, activity and sleep, while connected medical devices can provide repeated measurements such as blood pressure from home.
AI could potentially analyse these longitudinal data alongside clinical records and pregnancy history.
But consumer data introduce additional problems.
Measurements can vary in quality. Data can be incomplete. False alerts can cause unnecessary anxiety or assessment.
A wearable collecting physiological information should therefore not be assumed to predict pregnancy complications simply because its software incorporates AI.
Clinical claims require appropriate evidence and validation.
Could AI Make Existing Health Inequalities Worse?
Yes.
Algorithms learn from their training data. If particular populations are poorly represented, performance can be worse for those groups.
WHO’s work on AI in sexual and reproductive health specifically identifies bias, privacy, unequal digital access, lack of transparency and reduced accuracy for underrepresented populations as potential risks. (World Health Organization)
This is particularly important in maternal health because existing healthcare disparities can become embedded in historical datasets.
An algorithm may therefore learn patterns created partly by unequal access to testing, referral or treatment rather than purely biological differences.
Responsible maternal AI requires validation across the populations in which it will actually be used.
Does AI Replace Your Doctor, Midwife or Pregnancy Screening?
No.
A prediction model does not examine you, understand every clinical circumstance or independently determine appropriate treatment.
It also cannot turn probability into certainty.
AI is most defensible when it supports clinical judgement—organising complex information, identifying patterns or flagging pregnancies that may warrant closer assessment.
WHO’s approach to healthcare AI emphasises human oversight, safety, transparency, equity and continuing evaluation. (World Health Organization)
Routine antenatal appointments, established pregnancy screening and clinical assessment remain essential.
When to Seek Medical Assessment
Do not wait for an app, wearable or AI risk score if you develop concerning pregnancy symptoms.
Seek appropriate maternity or emergency assessment for symptoms such as severe headache, visual disturbance, significant abdominal pain, vaginal bleeding, fluid loss, seizures, difficulty breathing or a concerning change in fetal movement later in pregnancy.
Follow the urgent-care guidance provided by your maternity service.
If a digital tool produces a high-risk result, ask an appropriately qualified healthcare professional what the result actually measures, whether the tool has been clinically validated and whether it should change your care.
The Bigger Picture
The most important development in pregnancy AI may not be a machine that diagnoses complications before clinicians can.
Its greater value may be identifying risk earlier, combining complex information more effectively and directing clinical attention toward pregnancies most likely to need it.
The evidence is promising, but the gap between research performance and dependable real-world care remains significant.
For women, the distinction is important: “AI-powered” does not mean clinically proven.
The meaningful questions are what the system predicts, which populations it was validated in, how often it misses cases, how often it produces false alarms and whether acting on its prediction improves outcomes.
AI may become increasingly important in maternal healthcare. Its role should be to make pregnancy care better informed—not to remove clinical judgement.
FAQs
Can AI predict pre-eclampsia before symptoms appear?
Potentially. Research models have shown promising ability to identify increased pre-eclampsia risk using clinical information and biomarkers before the condition becomes apparent, although external validation remains limited for many models. (PubMed)
Can AI predict gestational diabetes early in pregnancy?
Research suggests machine-learning models can identify increased risk using combinations of maternal and clinical factors. These systems do not currently replace established diagnostic testing. (PubMed)
How accurate is AI at predicting pregnancy complications?
Accuracy varies substantially by condition, dataset and population. Some research models report AUC values above 0.9, but performance can change considerably when models are tested in different clinical settings. (PubMed)
Can my smartwatch predict pregnancy complications?
Not simply because it collects health information or uses AI. A device needs appropriate clinical validation for the specific complication it claims to predict.
Should I trust an AI pregnancy risk score?
Treat it as information requiring clinical context rather than a diagnosis. Ask whether the model has been independently validated and whether its result should change your clinical care.
Secure Your Position
Verified Sistas receive a free Pink Tick, signalling genuine, trusted identity across Sistapedia.
Women come to Sistapedia to learn. Professionals come to educate.
If you’re a qualified Medical Doctor, Registered Nurse, Healthcare Practitioner, Expert, Product or Brand in women’s health, select the Verification tab in your profile and apply for Crown Verification.
Start publishing content, join our global directories, and secure your position among the trusted leaders shaping the future of women’s health.
Select the Verification tab in your profile to get started.
Sources
2025 systematic review — Artificial Intelligence Applications in Obstetric Risk Prediction: A Systematic Review of Machine Learning Models for Preeclampsia. (PubMed)
2025 systematic review — Artificial Intelligence for Early Detection of Preeclampsia and Gestational Diabetes Mellitus: A Systematic Review of Diagnostic Performance. (PubMed)
2026 systematic review and meta-analysis — Machine Learning Prediction Models for Preeclampsia. (PubMed)
World Health Organization — Artificial Intelligence for Health and guidance on AI in sexual and reproductive health. (World Health Organization)
Medical disclaimer: This article provides general educational information and does not replace personalised medical advice, diagnosis or treatment. Speak with an appropriately qualified healthcare professional about symptoms, medicines, tests or treatment decisions.
Google