{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T17:49:31Z","timestamp":1789235371696,"version":"build-2803163510"},"reference-count":35,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u20132025) 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 &amp;gt;12\u2009000) covered 12 countries. LLM\/GPT-powered chatbots outperformed LLM\/GPT systems in conversational naturalness, topic diversity and user satisfaction (mean acceptability scores 85\u201394%). Core functions included real-time psychoeducation (n\u2009=\u200914 studies), mental health screening and behavioral activation (n\u2009=\u200911\/15), partner engagement (n\u2009=\u20096\/15), and predictive risk modeling for adverse outcomes (n\u2009=\u20099). 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\u00a0months, 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).<\/jats:p>","DOI":"10.1093\/oodh\/oqag001","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T12:25:02Z","timestamp":1767702302000},"source":"Crossref","is-referenced-by-count":2,"title":["Designing conversational intelligence: effect of large language models (GPT-driven) platforms for precision maternal and newborn health engagement: a systematic review"],"prefix":"10.1093","volume":"4","author":[{"given":"Robab","family":"Rasoli","sequence":"first","affiliation":[{"name":"School of Nursing and Midwifery, Department of Midwifery, Zanjan University of Medical Sciences , Zanjan ,","place":["Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahimeh","family":"Ebrahimisadrabadi","sequence":"additional","affiliation":[{"name":"Department of Economics, University of New Hampshire , Durham, NH ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Khedri","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Ziaeian Hospital, Tehran University of Medical Sciences , Tehran ,","place":["Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Solmaz","family":"Sohrabei","sequence":"additional","affiliation":[{"name":"Student Research Committee, Department and Faculty of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences , Tehran ,","place":["Iran"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"2026071703534184800_ref1","doi-asserted-by":"publisher","first-page":"105","DOI":"10.59888\/ajosh.v1i03.19","article-title":"The physiological changes in the postpartum period after childbirth","volume":"1","author":"Martin","year":"2022","journal-title":"Asian J Soc Humanit"},{"key":"2026071703534184800_ref2","doi-asserted-by":"publisher","first-page":"H191","DOI":"10.1152\/ajpheart.00055.2024","article-title":"Guidelines for assessing maternal cardiovascular physiology during pregnancy and postpartum","volume":"327","author":"Collins","year":"2024","journal-title":"Am J Phys Heart Circ Phys"},{"key":"2026071703534184800_ref3","doi-asserted-by":"publisher","first-page":"743","DOI":"10.7326\/M16-2956","article-title":"Development of a research agenda to identify evidence-based strategies to improve physician wellness and reduce burnout","volume":"166","author":"Dyrbye","year":"2017","journal-title":"Ann Intern Med"},{"key":"2026071703534184800_ref4","doi-asserted-by":"publisher","first-page":"1169595","DOI":"10.3389\/frai.2023.1169595","article-title":"ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations","volume":"6","author":"Dave","year":"2023","journal-title":"Front Artif Intell"},{"key":"2026071703534184800_ref5","doi-asserted-by":"publisher","first-page":"240","DOI":"10.4018\/979-8-3693-1214-8.ch012","volume-title":"Emerging Technologies for Health Literacy and Medical Practice","author":"Aminabee","year":"2024"},{"key":"2026071703534184800_ref6","doi-asserted-by":"publisher","first-page":"e40789","DOI":"10.2196\/40789","article-title":"Artificial intelligence\u2013based chatbots for promoting health behavioral changes: systematic review","volume":"25","author":"Aggarwal","year":"2023","journal-title":"J Med Internet Res"},{"key":"2026071703534184800_ref7","doi-asserted-by":"publisher","first-page":"100165","DOI":"10.1016\/j.xagr.2023.100165","article-title":"Feasibility and impact of a mental health chatbot on postpartum mental health: a randomized controlled trial","volume":"3","author":"Suharwardy","year":"2023","journal-title":"AJOG Global Reports"},{"key":"2026071703534184800_ref8","doi-asserted-by":"publisher","first-page":"e54737","DOI":"10.2196\/54737","article-title":"Artificial intelligence\u2013augmented clinical decision support systems for pregnancy care: systematic review","volume":"26","author":"Lin","year":"2024","journal-title":"J Med Internet Res"},{"key":"2026071703534184800_ref9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.56781\/ijsrmd.2024.3.2.0034","article-title":"Improving healthcare decision-making with predictive analytics: a conceptual approach to patient risk assessment and care optimization","volume":"3","author":"Edoh","year":"2024","journal-title":"Int J Scholar Res Med Dent"},{"key":"2026071703534184800_ref10","first-page":"20","article-title":"Optimizing treatment planning and patient outcomes: the role of advanced analytics and personalized approaches in healthcare","volume":"2","author":"Malik","year":"2023","journal-title":"Int J Mod Dev Eng Sci"},{"key":"2026071703534184800_ref11","doi-asserted-by":"publisher","first-page":"e51712","DOI":"10.2196\/51712","article-title":"The potential of chatbots for emotional support and promoting mental well-being in different cultures: mixed methods study","volume":"25","author":"Chin","year":"2023","journal-title":"J Med Internet Res"},{"key":"2026071703534184800_ref12","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1109\/OJCS.2023.3300321","article-title":"A survey on ChatGPT: AI-generated contents, challenges, and solutions","volume":"4","author":"Wang","year":"2023","journal-title":"IEEE Open J Comput Soc"},{"key":"2026071703534184800_ref13","doi-asserted-by":"publisher","first-page":"77","DOI":"10.30630\/joiv.7.1.1595","article-title":"Chatbot for diagnosis of pregnancy disorders using artificial intelligence markup language (AIML)","volume":"7","author":"Rahmatulloh","year":"2023","journal-title":"JOIV: Int J Inform Visualization"},{"key":"2026071703534184800_ref14","first-page":"28","article-title":"Development of health Chatbot android-based application for pregnant women using natural language processing","volume":"1","author":"Arsita","year":"2025","journal-title":"JIEngS"},{"key":"2026071703534184800_ref15","doi-asserted-by":"publisher","first-page":"e58653","DOI":"10.2196\/58653","article-title":"A chatbot (Juno) prototype to deploy a behavioral activation intervention to pregnant women: qualitative evaluation using a multiple case study","volume":"8","author":"Mancinelli","year":"2024","journal-title":"JMIR Form Res"},{"key":"2026071703534184800_ref16","doi-asserted-by":"publisher","first-page":"e72469","DOI":"10.2196\/72469","article-title":"Chatbot to support the mental health needs of pregnant and postpartum women (moment for parents): design and pilot study","volume":"9","author":"McAlister","year":"2025","journal-title":"JMIR Form Res"},{"key":"2026071703534184800_ref17","doi-asserted-by":"publisher","first-page":"e18607","DOI":"10.2196\/18607","article-title":"A chatbot for perinatal women\u2019s and partners\u2019 obstetric and mental health care: development and usability evaluation study","volume":"9","author":"Chung","year":"2021","journal-title":"JMIR Med Inform"},{"key":"2026071703534184800_ref18","doi-asserted-by":"publisher","first-page":"e56807","DOI":"10.2196\/56807","article-title":"Development and refinement of a chatbot for birthing individuals and newborn caregivers: mixed methods study","volume":"7","author":"Rivera","year":"2024","journal-title":"JMIR Pediatr Parent"},{"key":"2026071703534184800_ref19","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1038\/s41598-023-50884-w","article-title":"Evaluation of the reliability and readability of ChatGPT-4 responses regarding hypothyroidism during pregnancy","volume":"14","author":"Onder","year":"2024","journal-title":"Sci Rep"},{"key":"2026071703534184800_ref20","doi-asserted-by":"publisher","DOI":"10.38124\/ijisrt\/IJISRT24APR697","article-title":"A Chatbot system for supporting women and families during pregnancy, international journal of innovative science and research","volume":"9","author":"Bhaskar","year":"2024","journal-title":"Technology."},{"key":"2026071703534184800_ref21","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1016\/j.procs.2023.01.411","article-title":"GISSA intelligent chatbot experience\u2014how effective was the interaction between pregnant women and a chatbot during the COVID-19 pandemic?","volume":"219","author":"Silveira","year":"2023","journal-title":"Procedia Comput Sci"},{"key":"2026071703534184800_ref22","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3390\/informatics9040088","volume-title":"InInformatics","author":"Puspitasari","year":"2022"},{"key":"2026071703534184800_ref23","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2308.07407","article-title":"Development and evaluation of three chatbots for postpartum mood and anxiety disorders","volume":"1","author":"Yao","journal-title":"arXiv preprint arXiv:230807407"},{"key":"2026071703534184800_ref24","doi-asserted-by":"publisher","first-page":"100072","DOI":"10.1016\/j.health.2022.100072","article-title":"Evaluating the use of chatbot during pregnancy: a usability study","volume":"2","author":"Montenegro","year":"2022","journal-title":"Healthcare Analytics"},{"key":"2026071703534184800_ref25","doi-asserted-by":"publisher","first-page":"100261","DOI":"10.1016\/j.ibmed.2025.100261","article-title":"Development of a bilingual healthcare chatbot for pregnant women: a comparative study of deep learning models with BiGRU optimization","volume":"12","author":"Kaneho","year":"2025","journal-title":"Intell-Based Med"},{"key":"2026071703534184800_ref26","article-title":"MAMA BOT: a system based on ML, NLP and IOT for supporting women and families during pregnancy","volume":"8","author":"Vinutha","year":"2023","journal-title":"IJNRD"},{"key":"2026071703534184800_ref27","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1590\/1413-81232021265.04072021","article-title":"Desenvolvimento e avalia\u00e7\u00e3o do prot\u00f3tipo da aplica\u00e7\u00e3o GISSA ChatBot Mam\u00e3e-Beb\u00ea para promo\u00e7\u00e3o da sa\u00fade infantil","volume":"26","author":"Barreto","year":"2021","journal-title":"Ci\u00eanc Sa\u00fade Colet"},{"key":"2026071703534184800_ref28","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.jhtm.2023.06.022","article-title":"Autonomous travel decision-making: an early glimpse into ChatGPT and generative AI","volume":"56","author":"Wong","year":"2023","journal-title":"J Hosp Tour Manag"},{"key":"2026071703534184800_ref29","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s10209-024-01118-x","article-title":"A systematic review of chatbots in inclusive healthcare: insights from the last 5 years","volume":"24","author":"Grassini","year":"2025","journal-title":"Univ Access Inf Soc"},{"key":"2026071703534184800_ref30","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2311.06898","article-title":"Retrieval and generative approaches for a pregnancy Chatbot in Nepali with stemmed and non-stemmed data: a comparative study","volume":"5","author":"Poudel","journal-title":"arXiv preprint arXiv:231106898"},{"key":"2026071703534184800_ref31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3706598.3713680","author":"Ramjee","year":"2025","journal-title":"Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems"},{"key":"2026071703534184800_ref32","doi-asserted-by":"publisher","DOI":"10.1201\/9781003517245","volume-title":"Large language models: concepts, techniques and applications","author":"Atkinson-Abutridy","year":"2024"},{"key":"2026071703534184800_ref33","volume-title":"Quick start guide to large language models: strategies and best practices for using ChatGPT and other LLMs","author":"Ozdemir","year":"2023"},{"key":"2026071703534184800_ref34","article-title":"Large language models (LLMs)","volume":"14","author":"Johnsen","year":"2024","journal-title":"Maria Johnsen"},{"key":"2026071703534184800_ref35","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1016\/j.procs.2025.04.030","article-title":"Comparison of rule-based chat bots with different machine learning models","volume":"259","author":"Jain","year":"2025","journal-title":"Procedia Comput Sci"}],"container-title":["Oxford Open Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/oodh\/advance-article-pdf\/doi\/10.1093\/oodh\/oqag001\/66292954\/oqag001.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/oodh\/article-pdf\/doi\/10.1093\/oodh\/oqag001\/66292954\/oqag001.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/oodh\/article-pdf\/doi\/10.1093\/oodh\/oqag001\/66292954\/oqag001.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T07:53:49Z","timestamp":1784274829000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/oodh\/article\/doi\/10.1093\/oodh\/oqag001\/8416480"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1093\/oodh\/oqag001","relation":{},"ISSN":["2754-4591"],"issn-type":[{"value":"2754-4591","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026]]},"published":{"date-parts":[[2026]]},"article-number":"oqag001"}}