Background: The use of digital health technologies, such as chatbots and virtual assistants based on artificial intelligence, has grown significantly in maternal health. These tools have been applied at different stages of the pregnancy and postpartum cycle, with emphasis on health education, risk monitoring, breastfeeding support, and mental healthcare. Despite their potential, challenges remain regarding clinical effectiveness, equity of access, and ethical safety. The aim of this study was to map the available evidence regarding the applications of artificial intelligence in pregnancy, childbirth, and postpartum care, with particular emphasis on conversational agents, virtual assistants, machine learning models, and clinical decision support systems, as well as their potential benefits, limitations, ethical implications, and implementation challenges. Methods: A scoping review was conducted between August and September 2025 in PubMed, Scopus, Web of Science, Nursing Database, Latin American and Caribbean Literature in Health Sciences, and Google Scholar. Original articles, reviews, experience reports, and theoretical reflections published in Portuguese, English, or Spanish that addressed the use of digital tools based on artificial intelligence in the pregnancy and postpartum cycle were included. The analysis of the studies was organized into five thematic axes: applications in prenatal care; support during childbirth; postpartum monitoring and breastfeeding; perinatal mental health; and ethical, technical, and implementation challenges. Results: The search identified 6230 records, of which 13 studies were included. Findings indicate that chatbots and virtual assistants show good acceptability, promote health education, and contribute to the early detection of physical and emotional risks. However, most of the studies are exploratory, with methodological limitations, lack of clinical trials, and limited validation in socially vulnerable contexts. Conclusions: Chatbots and artificial intelligence tools are promising resources for perinatal care, particularly as a complement to face-to-face attention. Their consolidation requires robust studies, cost-effectiveness assessments, and public policies that ensure equity and digital inclusion.

Background: The use of digital health technologies, such as chatbots and virtual assistants based on artificial intelligence, has grown significantly in maternal health. These tools have been applied at different stages of the pregnancy and postpartum cycle, with emphasis on health education, risk monitoring, breastfeeding support, and mental health care. Despite their potential, challenges remain regarding clinical effectiveness, equity of access, and ethical safety. To describe, through a narrative literature review, the use of chatbots, conversational agents, and virtual assistants as support during pregnancy, childbirth, and the postpartum period, identifying applications, benefits, limitations, and perspectives for health practice. Methods: A narrative literature review was conducted between August and September 2025 in PubMed, Scopus, Web of Science, Nursing Database, Latin American and Caribbean Literature in Health Sciences and Google Scholar. Original articles, reviews, experience reports, and theoretical reflections published in Portuguese, English, or Spanish that addressed the use of digital tools based on artificial intelligence in the pregnancy and postpartum cycle were included. The analysis of the studies was organized into five thematic axes: applications in prenatal care, support during childbirth, postpartum monitoring and breastfeeding, perinatal mental health, and ethical, technical, and implementation challenges. Results: The search identified 6,230 records, of which 13 studies were included. Findings indicate that chatbots and virtual assistants show good acceptability, promote health education, and contribute to the early detection of physical and emotional risks. However, most of the studies are exploratory, with methodological limitations, lack of clinical trials, and limited validation in socially vulnerable contexts. Conclusions: Chatbots and artificial intelligence tools are promising resources for perinatal care, particularly as a complement to face-to-face attention. Their consolidation requires robust studies, cost-effectiveness assessments, and public policies that ensure equity and digital inclusion.

Background: Maternal mortality remains a major global health challenge, disproportionately affecting black and Indigenous women. Hypertensive disorders of pregnancy and postpartum hemorrhage are leading direct causes of maternal death. Artificial intelligence (AI) tools have emerged as potential strategies for predicting these complications, yet concerns persist about their equity and validation across racial groups. Methods: A rapid review was conducted in five databases PubMed, EMBASE, Web of Science, Scopus and LILACS to synthesize recent evidence on the use of AI for preventing maternal mortality due to hypertension and postpartum hemorrhage. Studies published in the last five years that included racial or ethnic data were selected and analyzed narratively. Results: Ten studies met the inclusion criteria, showing high predictive accuracy of AI models (AUROC often >0.95) for severe maternal outcomes. However, few models incorporated racial variables or underwent external validation in racially diverse or low-resource populations. Evidence suggests that unrepresentative datasets may perpetuate or exacerbate existing health inequities. Conclusions: AI demonstrates strong technical performance in predicting maternal complications but limited equity in application. Broader racial representation, external validation, and ethical governance are essential for ensuring that AI-based tools reduce rather than reinforce racial disparities in maternal mortality.

Identificar as evidências científicas sobre competência tecnológica e seus impactos na eficiência organizacional e na gestão em saúde digital. Método: A revisão será conduzida conforme as diretrizes do Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols e as recomendações metodológicas de Souza, Silva e Carvalho e do Joanna Briggs Institute. A questão de pesquisa foi estruturada com base no acrônimo PICo: População; Intervenção; Comparação; Desfecho. Para identificar estudos publicados sobre o tema desta revisão, serão utilizadas as bases de dados Scientific Electronic Library Online, Medical Literature Analysis and Retrieval System on-line, Cumulative Index to Nursing and Allied Health Literature, Literatura Latino-Americana e do Caribe em Ciências da Saúde e Base de Dados em Enfermagem. A seleção dos estudos será realizada por dois revisores independentes, e a análise seguirá abordagem temática e descritiva, com classificação da qualidade metodológica segundo o Joanna Briggs Institute e do nível de evidência conforme o Oxford Centre for Evidence-Based Medicine. Resultados: Os resultados serão apresentados por meio de quadros e tabelas; além disso, os principais achados dos estudos poderão ser transcritos e organizados com o auxílio do Microsoft Excel.

Background: Maternal mortality remains a major global health challenge, disproportionately affecting black and Indigenous women. Hypertensive disorders of pregnancy and postpartum hemorrhage are the leading direct causes of maternal death. Artificial intelligence (AI) tools have emerged as potential strategies for predicting these complications, yet concerns persist about their equity and validation across racial groups. Methods: A rapid review was conducted in five databases, PubMed, EMBASE, Web of Science, Scopus and LILACS, to synthesize recent evidence on the use of AI for preventing maternal mortality due to hypertension and postpartum hemorrhage. Studies published in the last five years that included racial or ethnic data were selected and analyzed narratively. Results: Ten studies met the inclusion criteria, showing high predictive accuracy of AI models (AUROC often >0.95) for severe maternal outcomes. However, few models incorporated racial variables or underwent external validation in racially diverse or low-resource populations. Evidence suggests that unrepresentative datasets may perpetuate or exacerbate existing health inequities. Conclusions: AI demonstrates strong technical performance in predicting maternal complications but limited equity in application. Broader racial representation, external validation, and ethical governance are essential for ensuring that AI-based tools reduce rather than reinforce racial disparities in maternal mortality.