Role of Artificial Intelligence in Improving Maternal and Child Health Outcomes in Rural Settings in India: A Comprehensive Review
TL;DR
Context-sensitive, responsible AI deployment can significantly drive progress toward Sustainable Development Goal 3 (SDG-3), however, rigorous regulatory frameworks are urgently required to bridge the pilot-to-implementation chasm.
Abstract
Background: Maternal and Child Health (MCH) remains a critical public health priority in rural India, where structural healthcare disparities, severe workforce shortages, and infrastructural deficits contribute to elevated maternal and neonatal mortality rates. Artificial Intelligence (AI) presents a transformative paradigm to mitigate these inequities. Objectives: This narrative review systematically synthesizes recent literature (2020–2026) evaluating the clinical efficacy, deployment barriers, and systemic impacts of AI–driven tools within rural Indian MCH ecosystems. Methods: A comprehensive search was performed across PubMed, Scopus, Google Scholar, and Web of Science using localized thematic mesh terms. High-quality peer-reviewed articles tracking AI in antenatal, intrapartum, and postnatal frameworks were synthesized using thematic analysis. Results: AI utilities significantly optimize early diagnostics (e.g., pre-eclampsia, fetal distress) and scale frontline worker workflows via Clinical Decision Support Systems (CDSS). Interoperable architectures like the Ayushman Bharat Digital Mission (ABDM) have accelerated adoption, though algorithm bias, digital illiteracy, and erratic network infrastructures limit scalability. Conclusion: Context-sensitive, responsible AI deployment can significantly drive progress toward Sustainable Development Goal 3 (SDG-3). However, rigorous regulatory frameworks are urgently required to bridge the pilot-to-implementation chasm.