Harnessing Artificial Intelligence for Maternal and Child Digital Health in India: A Narrative Review
Abstract:
Artificial Intelligence (AI) and
digital health systems are transforming maternal and child healthcare through the
application of predictive analytics, risk stratification, remote monitoring,
and clinical decision support systems(CDSS) for better health outcomes. This
narrative review examines the integration of AI-enabled digital health
interventions in India’s Maternal and Child Health (MCH) systems, and
synthesizes the existing evidence and their impact on key areas of its application.
It also examines the structural, socio-behavioral, and ethical factors
influencing their implementation, informed by global, low- to middle-income
countries (LMIC), and India-specific evidence. Literature was obtained from
PubMed, Google Scholar, and Scopus (2015–2026) and supplemented by reports from
governmental and multilateral organizations. In India, digital health platforms
such as mMitra, SMART health pregnancy system, and telemedicine (eSanjeevani)
have demonstrated feasibility and acceptability in rural settings. Whereas
AI-enabled platform eSanjeevani CDSS have enhanced the quality of
teleconsultations and improved diagnostic accuracy for gestational diabetes
mellitus and pregnancy-induced hypertension at the primary healthcare level.
Ayushman Bharat Digital Mission (ABDM), supported by over 863 million Ayushman
Bharat Health Accounts (ABHA), establishes the interoperability infrastructure
necessary for the integration of AI. For the responsible use of AI in
Healthcare, two initiatives have been launched by the Government of India: the
Strategy for Artificial Intelligence in Healthcare (SAHI) and Benchmarking Open
Data Platform for Health (BODH) in February 2026. Review concludes that
responsible AI deployment, grounded in ethical governance, equity, and
context-sensitive adaptation, is essential to achieve Sustainable Development
Goal 3 (SDG 3) and universal quality MCH care in India.
References:
[1]. The-Sustainable-Development-Goals-Report-2025.pdf. 2025, Date of
access – 26/03/2026 https://unstats.un.org/sdgs/report/2025/The-Sustainable-Development-Goals-Report-2025.pdf
[2]. Bossman, E., Johansen, M. A., & Zanaboni, P., 2022, mHealth
interventions to reduce maternal and child mortality in Sub-Saharan Africa and
Southern Asia: A systematic literature review. Frontiers in Global Women’s
Health, 3, 942146. https://doi.org/10.3389/fgwh.2022.942146
[3]. Kim, H. Y., Cho, G. J., & Kwon, H. S., 2023, Applications of
artificial intelligence in obstetrics. Ultrasonography (Seoul, Korea), 42(1),
2–9. https://doi.org/10.14366/usg.22063
[4]. Rocha, E. D. S., Melo, F. L. D. M., De Mello, M. E. F., Figueirôa, B.,
Sampaio, V., & Endo, P. T., 2022 September 23, On usage of artificial
intelligence for predicting mortality during and post-pregnancy: a
systematic review of literature. https://doi.org/10.21203/rs.3.rs-1959423/v1
[5]. Pradeep
Kumar Panda, Rahul Sharma. 2024, Transforming maternal
healthcare: Harnessing the power of artificial intelligence for improved
outcomes and access. World Journal of Advanced Research and Reviews, 23(1),
662–668. https://doi.org/10.30574/wjarr.2024.23.1.2005
[6]. Classification of Digital Health Interventions v1.0, WHO, Geneva., 2020,
Retrieved from https://iris.who.int/server/api/core/bitstreams/5ed36c71-bc74-41e9-8751-ca8bd9a8d823/content
[7]. Cockburn, N., Osborne, C., Withana, S., Elsmore, A., Nanjappa, R.,
South, M., Nirantharakumar, K., 2024, Clinical decision support systems for
maternity care: a systematic review and meta-analysis. eClinicalMedicine,
76, 102822. https://doi.org/10.1016/j.eclinm.2024.102822
[8]. Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R., 2018,
Artificial intelligence (AI) and global health: how can AI contribute to health
in resource-poor settings? BMJ Global Health, 3(4), e000798.
https://doi.org/10.1136/bmjgh-2018-000798
[9]. Gore, M. N., & Olawade, D. B., 2024, Harnessing AI for public
health: India’s roadmap. Frontiers in Public Health, 12, 1417568.
https://doi.org/10.3389/fpubh.2024.1417568
[10]. Olawade, D. B., Wada, O. J., David-Olawade, A. C., Kunonga, E., Abaire,
O., & Ling, J., 2023, Using artificial intelligence to improve public
health: a narrative review. Frontiers in Public Health, 11,
1196397. https://doi.org/10.3389/fpubh.2023.1196397
[11]. Panch, T., Mattie, H., & Atun, R., 2019, Artificial intelligence and
algorithmic bias: Implications for health systems. Journal of Global Health,
9(2), 010318. https://doi.org/10.7189/jogh.09.020318
[12]. Murthy, N., Chandrasekharan, S., Prakash, M. P., Ganju, A., Peter, J.,
Kaonga, N., & Mechael, P., 2020, Effects of an mHealth voice message
service (mMitra) on maternal health knowledge and practices of low-income women
in India: findings from a pseudo-randomized controlled trial. BMC Public
Health, 20(1), 820. https://doi.org/10.1186/s12889-020-08965-2
[13]. Venkatesh, V., Rai, A., Georgia State University, Sykes, T. A.,
Aljafari, R., & University of Arkansas, 2016, Combating Infant Mortality in
Rural India: Evidence from a Field Study of eHealth Kiosk Implementations. MIS
Quarterly, 40(2), 353–380. https://doi.org/10.25300/MISQ/2016/40.2.04
[14]. Bajwa, J., Munir, U., Nori, A., & Williams, B., 2021, Artificial
intelligence in healthcare: Transforming the practice of medicine. Future
Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095
[15]. Das, S. K., Dasgupta, R. K., Roy, S. D., & Shil, D., 2024, AI in
Indian healthcare: From roadmap to reality. Intelligent Pharmacy, 2(3),
329–334. https://doi.org/10.1016/j.ipha.2024.02.005
[16]. Annual Report 2023 24 DoHFW English_0.pdf., 2023, Retrieved from https://www.mohfw.gov.in/sites/default/files/Annual%20Report%202023%2024%20DoHFW%20English_0.pdf
[17]. Alzubaidi, M., Agus, M., Alyafei, K., Althelaya, K. A., Shah, U.,
Abd-Alrazaq, A., Househ, M., 2022, Toward deep observation: A systematic survey
on artificial intelligence techniques to monitor fetus via ultrasound images. iScience,
25(8), 104713. https://doi.org/10.1016/j.isci.2022.104713
[18]. Union Minister of Health and Family Welfare Shri Jagat Prakash Nadda
Launches SAHI and BODH Initiatives to Strengthen Responsible Health AI
Ecosystem at the India AI Impact Summit., 2026, Date of access March 21
2026, from https://www.pib.gov.in/www.pib.gov.in/Pressreleaseshare.aspx?PRID=2229226
[19]. Topol, E. J., 2019, High-performance medicine: the convergence of human
and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
[20]. Artifical Intelligence in Global Health_Defining a Collective Path
Forward. 2023, Date of access – 29/03/2026. https://digital.library.unt.edu/ark:/67531/metadc2289561/m2/1/high_res_d/AI-in-Global-Health_webFinal_508.pdf
[21]. Cahyo, L. M., & Astuti, S. D., 2023, Early Detection of Health
Problems through Artificial Intelligence (Ai) Technology in Hospital
Information Management: A Literature Review Study. Journal of Medical and
Health Studies, 4(3), 37–42. https://doi.org/10.32996/jmhs.2023.4.3.5
[22]. National Health Policy 2017., 2017, Date of access – 23/03/2026,
Retrieved from https://www.mohfw.gov.in/sites/default/files/9147562941489753121.pdf
[23]. Mishra, U. S., Yadav, S., & Joe, W., 2024, The Ayushman Bharat
Digital Mission of India: An Assessment. Health Systems & Reform, 10(2),
2392290. https://doi.org/10.1080/23288604.2024.2392290
[24]. Jain, D., 2023, Regulation of Digital Healthcare in India: Ethical and
Legal Challenges. Healthcare, 11(6), 911. https://doi.org/10.3390/healthcare11060911
[25]. National Health Authority_Annual_Report_31-12-2025.cdr. 2025, Date of
access – 21/03/2026 Retrieved from https://nha.gov.in/strapi/uploads/NHA_Annual_Report_2024_25_e9dfbac7cd_90dae48f7a.pdf
[26]. Update on Secure AI in Health Initiative. 2026, Retrieved March 23,
2026, from https://www.pib.gov.in/www.pib.gov.in/Pressreleaseshare.aspx?PRID=2237406
[27]. Government notifies DPDP Rules to empower citizens and protect privacy.
2025, Retrieved March 22, 2026, from https://www.pib.gov.in/www.pib.gov.in/Pressreleaseshare.aspx?PRID=2190014
[28]. Geneva:World Health Organization. 2021, Ethics and Governance of
Artificial Intelligence for Health: WHO Guidance (1st ed.). Geneva: World
Health Organization. Retrieved from https://iris.who.int/server/api/core/bitstreams/f780d926-4ae3-42ce-a6d6-e898a5562621/content
[29]. AI_Health_Compendium_2026_5604af298c.pdf. 2025, Retrieved from https://d19ob9sqegt2wc.cloudfront.net/stage/uploads/AI_Health_Compendium_2026_5604af298c.pdf
[30]. Vatsa, R., Chang, W., Akinyi,
S., Little, S., Gakii, C., Mungai, J., McConnell,
M., 2025, Impact evaluation of a digital health platform empowering Kenyan
women across the pregnancy-postpartum care continuum: A cluster randomized
controlled trial. PLOS Medicine, 22(2), e1004527. https://doi.org/10.1371/journal.pmed.1004527
[31]. Baglivo, F., De Angelis, L., Casigliani, V., Arzilli, G., Privitera, G.
P., & Rizzo, C., 2023, Exploring the Possible Use of AI Chatbots in Public
Health Education: Feasibility Study. JMIR Medical Education, 9,
e51421. https://doi.org/10.2196/51421
[32]. LeFevre, A. E., Shah, N., Scott, K., Chamberlain, S., Ummer, O.,
Bashingwa, J. J. H., Mohan, D., 2022, The impact of a direct to beneficiary
mobile communication program on reproductive and child health outcomes: a
randomised controlled trial in India. BMJ Global Health, 6(Suppl
5), e008838. https://doi.org/10.1136/bmjgh-2022-008838
[33]. Nagraj, S., Kennedy, S., Jha, V., Norton, R., Hinton, L., Billot, L.,
Hirst, J., 2023, A Mobile Clinical Decision Support System for High-Risk
Pregnant Women in Rural India (SMARThealth Pregnancy): Pilot Cluster Randomized
Controlled Trial. JMIR Formative Research, 7, e44362. https://doi.org/10.2196/44362
[34]. Transforming healthcare delivery through Artificial intelligence., 2026,
Retrieved from https://static.pib.gov.in/WriteReadData/specificdocs/documents/2026/feb/doc2026213788701.pdf
[35]. AI-Enabled System Powers 1,000+ Hospitals and Reaches Half a Million
Mothers., 25 December 2025, Retrieved from https://frontiertech.niti.gov.in/story/ai-enabled-system-powers-1000-hospitals-and-reaches-half-a-million-mothers/
[36]. Wearable, Cloud-Connected System Saves 20000 Newborn Lives., 2025,
Retrieved from https://frontiertech.niti.gov.in/story/wearable-cloud-connected-monitoring-system-saves-20000-high-risk-newborn-lives/
[37]. Ethical Guidelines for application of Artificial Intelligence in
Biomedical Research and Healthcare.2023.ICMR.pdf., 2023, Retrieved from https://www.icmr.gov.in/icmrobject/custom_data/pdf/Ethical-guidelines/Ethical_Guidelines_AI_Healthcare_2023.pdf
[38]. Britney Johnson., March 2025, The Ethical and Privacy Implications of
Using AI and Data Analytics in Maternal Health Monitoring. New York University:
ResearchGate. Retrieved from https://www.researchgate.net/publication/389943064_The_Ethical_and_Privacy_Implications_of_Using_AI_and_Data_Analytics_in_Maternal_Health_Monitoring
[39]. World Health Organization., 2019, WHO guideline: Recommendations on
digital interventions for health system strengthening. Geneva: World Health
Organization.
[40]. Tandon, U., & Gupta, N. K., 2025, Informational Privacy in the Age of Artificial Intelligence: A Critical Analysis of India’s DPDP Act, 2023. Legal Issues in the Digital Age, 6(2), 87–117. https://doi.org/10.17323/2713-2749.2025.2.87.117
