About this Research Topic
Maternal mortality is a sentinel event used to monitor maternal health globally. It is estimated that 810 women die every day due to pregnancy-related complications across the globe. The maternal mortality ratio (MMR) in high-income countries is 15/100,000 live births, while that in low-middle-income countries (LMICs) is 298/100,000 live births. However, death only reflects the tip of the iceberg. For each maternal death, severe maternal morbidity (SMM) may affect 50-100 women with a significant impact on their physical, psychological, social, and economic well-being.
Neonatal mortality has seen a decline in the past three decades globally. However, the neonatal mortality rates in LMICs are nine times higher than in high-income countries. The wide disparities in maternal and neonatal morbidity and mortality, both within and between countries or regions of the world, reflect inequities in access to quality care provided by adequately trained professionals, as well as the unequal distribution of risk factors for poor outcomes.
The use of artificial intelligence (AI) may help the development of new ways to address the global inequities in care provision and act as an enabler to improve maternal and neonatal health in LMICs. With the wide penetration of cellular technologies, electronic health records, and digital health applications, African countries, and others, such as Pakistan, India, and Afghanistan, are exploring how existing data on maternal and child health services can be utilized for data-driven insights obtained through AI.
The goal of this Research Topic is to highlight the use of AI in maternal and neonatal health conditions, with particular emphasis on LMICs. We are interested in assessing how AI may be leveraged to: identify high-risk pregnancies earlier; provide clinical decision support for frontline care providers in general, and specifically physicians who are working in resource-constrained settings; and identify novel clinical or biomarker-based indicators of maternal and perinatal health. With the exception of commentaries, we will consider all manuscripts related to maternal and/or perinatal morbidity and mortality that focus on prevention, prediction, diagnosis, or management using AI.
By consolidating evidence across regions, this research topic will be a useful resource for clinicians, public health specialists, and policymakers working in the field of maternal and newborn health. The focus of these manuscripts could include the following:
• Prevention of maternal and perinatal morbidity and mortality using AI
• Risk stratification tools
• Diagnosis and care pathways for improving maternal and perinatal health
• Evaluation of the short, medium, or long-term impact of AI in Maternal, Newborn & Child Health services
• Enablers and barriers for use of AI in resource-constrained settings with emphasis on maternal and perinatal health
Neonatal mortality has seen a decline in the past three decades globally. However, the neonatal mortality rates in LMICs are nine times higher than in high-income countries. The wide disparities in maternal and neonatal morbidity and mortality, both within and between countries or regions of the world, reflect inequities in access to quality care provided by adequately trained professionals, as well as the unequal distribution of risk factors for poor outcomes.
The use of artificial intelligence (AI) may help the development of new ways to address the global inequities in care provision and act as an enabler to improve maternal and neonatal health in LMICs. With the wide penetration of cellular technologies, electronic health records, and digital health applications, African countries, and others, such as Pakistan, India, and Afghanistan, are exploring how existing data on maternal and child health services can be utilized for data-driven insights obtained through AI.
The goal of this Research Topic is to highlight the use of AI in maternal and neonatal health conditions, with particular emphasis on LMICs. We are interested in assessing how AI may be leveraged to: identify high-risk pregnancies earlier; provide clinical decision support for frontline care providers in general, and specifically physicians who are working in resource-constrained settings; and identify novel clinical or biomarker-based indicators of maternal and perinatal health. With the exception of commentaries, we will consider all manuscripts related to maternal and/or perinatal morbidity and mortality that focus on prevention, prediction, diagnosis, or management using AI.
By consolidating evidence across regions, this research topic will be a useful resource for clinicians, public health specialists, and policymakers working in the field of maternal and newborn health. The focus of these manuscripts could include the following:
• Prevention of maternal and perinatal morbidity and mortality using AI
• Risk stratification tools
• Diagnosis and care pathways for improving maternal and perinatal health
• Evaluation of the short, medium, or long-term impact of AI in Maternal, Newborn & Child Health services
• Enablers and barriers for use of AI in resource-constrained settings with emphasis on maternal and perinatal health
Keywords: AI, Artificial Intelligence, Maternal Health, Neonatal Health
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
