India has lowered deaths of mothers and newborns in the last decade, but many lives are still lost because health workers cannot act early enough. AI is now being tested to spot danger signs sooner, giving clinicians a chance to intervene.
Key Developments
- Government data show the Maternal Mortality Ratio (MMR) fell from 130 per 1,00,000 live births (2014‑16) to 88 (2021‑23).
- The Neonatal Mortality Rate (NMR) dropped from 26 per 1,000 live births (2014) to 19 (2021).
- ARMMAN’s mMitra partnered with Google and IIT‑Madras to build a model that predicts which mothers will stop receiving messages. Pilot testing cut drop‑outs among high‑risk women by almost 30 %.
- The NFHS‑6 shows women getting at least four antenatal visits rose from 58.5 % to 65.2 %, still leaving many pregnancies under‑served.
Important Facts
Globally, the World Health Organization estimates 2,60,000 women died during pregnancy or childbirth in 2023, and UNICEF reports 2.3 million newborn deaths in the first month of life in 2024 (about 6,200 per day). These numbers highlight the cost of missed warnings.
Data sources that can feed predictive models include antenatal records, lab results, blood‑pressure trends, maternal age, anaemia status, obstetric history, birth weight, gestational age, facility data and social risk indicators.
Exam Relevance
Understanding how ASHA workers and other frontline staff use AI‑driven alerts links to GS‑2 topics on health governance, public‑health delivery, and the National Health Mission. The role of data‑driven decision‑support touches GS‑3 themes of technology adoption, digital governance and ethical use of AI, including bias mitigation and privacy safeguards.
Way Forward
- Build reliable data pipelines that integrate fragmented health records across public and private systems.
- Ensure interoperability and strict privacy safeguards before deploying models at scale.
- Validate algorithms clinically and monitor performance continuously, with clear accountability when predictions err.
- Pair predictions with actionable response mechanisms: extra home visits, timely referrals, transport arrangements, and adequate staffing at facilities.
- Audit models for bias, keep them explainable, and maintain human oversight so clinicians can query why a case is flagged.
When built on sound data, responsibly designed, and embedded in a responsive health system, AI can shift India’s maternal‑child care from reacting to preventing, thereby saving lives that would otherwise be lost.