Maatrisakhi: An AI-Based Maternal and Child Healthcare Companion for   ASHA Workers

Mrs. V Manjula1, Krithika M2, Kruthika B 3, Lavanya B R4, Lekhana K 5

Assistant Professor, Dept of Computer Science and Engineering, K.S Institute of Technology, Karnataka India1

Student, Dept of Computer Science and Engineering, K.S Institute of Technology, Karnataka, India2-5

ABSTRACT:Maternal and child healthcare continues to face significant challenges, particularly in rural and semi-urban regions where access to timely medical services and expert guidance is limited. Frontline healthcare workers, such as ASHA and Anganwadi workers, play a crucial role in monitoring maternal and neonatal health. However, their efforts are often constrained by manual data handling, lack of real-time insights, and limited technological support. This survey paper reviews recent advancements in the application of artificial intelligences and digital health systems for maternal care, focusing on risk prediction, health monitoring and decision support tools. Existing studies highlights the use of machine learning techniques for identifying high risk pregnancies, tracking vital health parameters, and improving early intervention strategies. Additionally various mobile- based healthcare solutions have been explored to enhance data collection, patient tracking, and communication between healthcare providers and beneficiaries. Despite these developments, several challenges remain, including usability issues, lack of multilingual and voice-enabled interfaces, and difficulties in ensuring continuous care from pregnancy to postnatal stages. This paper identifies these research gaps and discuss the need for an integrated, user-friendly system that supports healthcare workers in real-time monitoring, risk assessment, and follow-up care. The survey further emphasizes the importance of combining AI -driven analytics with features such as voice interaction, automated alerts and immunization tracking to improve healthcare delivery. By analyzing existing approaches and their limitations, this study provides a foundation for developing more efficient and accessible maternal and child health care support.