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Designing conversational intelligence: effect of large language models (GPT-driven) platforms for precision maternal and newborn health engagement: a systematic review
0
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4
Autoren
2026
Jahr
Abstract
= 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).
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