Designing conversational intelligence: effect of large language models (GPT-driven) platforms for precision maternal and newborn health engagement: a systematic review.

Maternal and newborn mortality remain stubbornly high in low-resource settings, driven by limited access to timely, personalized and emotionally supportive care during pregnancy. Large language models/generative pre-trained transformer (LLM/GPTs), particularly GPT-driven conversational agents, have emerged as scalable, versatile digital health tools capable of delivering evidence-based information, mental health support and risk stratification for complications such as preeclampsia, gestational diabetes and preterm birth. This systematic review aimed to synthesize global evidence on the design, implementation and effectiveness of LLM/GPT-powered chatbots for precision maternal and newborn health engagement. Following PRISMA 2020 guidelines, we searched MEDLINE, Embase, CINAHL, Web of Science, Inspec and IEEE Xplore from January 2015 to November 2025. We included 15 peer-reviewed studies (published 2021-2025) that developed or evaluated GPT-based or equivalent generative conversational agents for pregnant individuals or their partners. Quality appraisal used EQUATOR tools and artificial intelligence-specific frameworks; two reviewers independently assessed risk of bias. The 15 studies (total participants >12 000) covered 12 countries. LLM/GPT-powered chatbots outperformed LLM/GPT systems in conversational naturalness, topic diversity and user satisfaction (mean acceptability scores 85-94%). Core functions included real-time psychoeducation (n = 14 studies), mental health screening and behavioral activation (n = 11/15), partner engagement (n = 6/15), and predictive risk modeling for adverse outcomes (n = 9). LLM models achieved high diagnostic concordance with clinicians for gestational diabetes (area under the curve 0.88 to 0.94) and preeclampsia warning signs. User retention ranged from 62 to 78% over 6 months, with the strongest engagement among prim parous and underserved populations. No serious harms were reported. LLM/GPT-driven conversational agents represent a breakthrough in accessible, personalized maternal and newborn health support. They effectively bridge gaps in high-risk pregnancy, emotional care, risk detection and partner involvement while maintaining safety and cultural adaptability. These findings strongly support rapid integration of LLM/GPT-powered chatbots into routine antenatal care pathways, particularly in low- and middle-income settings. The study was registered with PROSPERO (CRD420251230253).