How AI could help reduce maternal and newborn health risks before they become emergencies

Most maternal and newborn emergencies do not arrive out of nowhere. Look back at a bad outcome and the warning signs are usually there in the weeks before: a missed antenatal visit, a lab result that should have prompted a call, blood pressure creeping up, severe anaemia, a mother who lives hours from the nearest facility equipped to help her. The trouble is that these clues sit in different places. One is in a paper register, another in a facility’s records, another in an ASHA worker’s notebook, another nowhere at all. By the time anyone joins them up, the window to act has often closed. This is the gap artificial intelligence is well suited to close, by pulling scattered signals together early enough for a health team to do something about them.

The need is not in doubt. The World Health Organization estimates that roughly 260,000 women died during pregnancy, childbirth or the weeks that followed in 2023. UNICEF puts newborn deaths in the first month of life at 2.3 million in 2024, and the neonatal period remains the most dangerous stretch of a child’s early life. India has made real headway here: the maternal mortality ratio has fallen to 88 per lakh live births in 2021-23, and neonatal mortality dropped from 26 per 1,000 live births in 2014 to 19 by 2021. Those numbers are proof that public health programmes change outcomes. They are also a reminder of how much still depends on spotting risk sooner.

What a predictive model brings to this is pattern recognition across data that no single clinician ever sees in full. Feed it antenatal records, lab values, maternal age, obstetric history, anaemia status, blood pressure trends, gestational age, birth-weight indicators, facility data and social risk factors, and it can surface the pregnancies that warrant closer watching. It can point to mothers likely to miss their next visit. It can pick up early signatures of pre-eclampsia, preterm birth, neonatal sepsis, respiratory distress or low birth weight, often before they would be obvious at a routine check.

The point isn’t to replace the doctor, nurse, midwife or community health worker. It is to help them decide where to look first. No resource-constrained system can give every high-risk pregnancy intensive review every single day; there are simply more mothers than hours. Triage is where AI earns its place. And a risk score is worth nothing on its own. It matters only when it sets something in motion: an extra antenatal visit, a referral, a diagnostic test, transport arranged, a facility told to be ready, closer monitoring after delivery.

Continuity of care is exactly where this bites. Recent government figures show the share of mothers receiving at least four antenatal visits rising from 58.5 per cent to 65.2 per cent. Encouraging, certainly, but it still leaves a large group without the follow-up they are meant to have. A system that can quietly flag who is drifting out of the care pathway gives frontline teams a chance to step in before that drift becomes a crisis.

The same thinking carries past delivery. A newborn’s first 28 days are the most fragile of all, and a model can weigh birth weight, gestational age, delivery complications, feeding patterns, temperature, infection risk and discharge details together to mark the babies who need a closer eye. Some groups are going further, testing machine learning that reads neonatal distress from vital signs, or even from the sound of a newborn’s cry. This work is early and unproven at scale, but the direction is clear: earlier, more consistent detection of newborn risk than human observation alone can manage.

None of this works on a shaky foundation. A model is only as good as the records under it, and public health data is frequently incomplete, late, biased or split across systems that do not talk to one another. Build on that and the output can mislead with an air of authority it has not earned. Dependable data pipelines, genuine interoperability, real privacy protection, clinical validation and monitoring that does not stop at launch are not optional extras here. They are the difference between better care and false confidence.

There is an equity dimension that deserves the same seriousness. Outcomes for mothers and babies turn on far more than clinical readings. They turn on nutrition, income, the distance to a facility, whether transport exists, schooling, family support, the state of local infrastructure. Train a model mostly on well-documented populations and it may perform worst precisely for the communities that need it most. So bias testing, explainability, human review and clear lines of accountability have to be built in from the start, not bolted on later.

Used well, AI belongs in maternal and newborn care as an early-warning and decision-support layer, nothing more and nothing less. It should help a health worker see risk sooner, understand why a particular mother or baby has been flagged, and judge what to do next. It should not flatten care into a number or hand clinical decisions to an algorithm. In this field above most, context counts for as much as prediction.

For a country like India, with a population this large and varied, that is where the real value sits: sharper outreach, better-targeted resources, faster response, risk caught before it hardens into an emergency. The technology only delivers any of this when it lives inside a working system of care, with trained teams, reliable data, sensible governance and the capacity to act on what the model says.

AI will not stop every maternal or newborn emergency. Built on trustworthy data and used with judgement by people who know what they are doing, it can prevent a real share of the harm that was avoidable to begin with. The future here was never going to be machines taking over from human care. It is health workers equipped with better intelligence, earlier warnings and stronger tools, reaching mothers and newborns before the emergency arrives.