Background: Maternal mortality remains a significant public health challenge in Brazil, reflecting persistent social, racial, and regional inequalities. Objectives: This study aimed to analyze trends and characteristics of maternal deaths in Brazil from 2000 to 2020, identifying sociodemographic and clinical risk factors. Methods: A retrospective, population-based study was conducted using secondary data from the Sistema de Informações sobre Mortalidade (SIM) of the Brazilian Ministry of Health. Descriptive and comparative statistical analyses were performed, including chi-square (χ²) goodness-of-fit tests and 95% confidence intervals calculated by the Wilson method. Variables were stratified by region, age, race, education, type and period of death, and investigation status. Results: A total of 40,907 maternal deaths were recorded. From 2000 to 2020, Brazil recorded 40,907 maternal deaths. The maternal mortality ratio decreased from approximately 74 deaths per 100,000 live births in 2000 to 57 per 100,000 in 2020, representing a 23% reduction over two decades. Most deaths occurred among young, Brown women with low education levels, particularly in the Southeast and Northeast regions. Direct obstetric causes accounted for two-thirds of cases, and most deaths occurred in hospital settings, mainly during the early puerperium. Conclusions: Despite progress, maternal mortality in Brazil remains above the WHO target and is strongly influenced by social inequities. Strengthening primary care, improving referral networks, expanding postpartum follow-up, and enhancing surveillance systems are essential for preventing avoidable deaths and achieving reproductive justice.

Background: Maternal mortality remains a significant public health challenge in Brazil, reflecting persistent social, racial, and regional inequalities. Objectives: This study aimed to analyze trends and characteristics of maternal deaths in Brazil from 2000 to 2020, identifying sociodemographic and clinical risk factors. Methods: A retrospective population-based cohort study was conducted using secondary data from the Mortality Information System. Descriptive and comparative statistical analyses were performed, stratifying variables such as region, age, ethnicity, education, type and period of death, and investigation status. Results: A total of 40,907 maternal deaths were recorded. Most deaths occurred among young, brown-skinned women with low education levels, particularly in the Southeast and Northeast regions. Direct obstetric causes were predominant, and most deaths occurred in hospital settings. Conclusions: Maternal mortality in Brazil is preventable and deeply influenced by structural inequities. Improving maternal care, enhancing surveillance, and addressing social determinants are critical for meeting Sustainable Development Goals and ensuring reproductive justice.

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.