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Background Pregnant women are considered a “high-risk” group with limited access to health facilities in urban slums in India. Barriers to using health services appropriately may lead to maternal and child mortality, morbidity, low birth weight, and children with stunted growth. With the increase in the use of artificial intelligence (AI) and machine learning in the health sector, we plan to develop a predictive model that can enable substantial uptake of maternal health services and improvements in adverse pregnancy health care outcomes from early diagnostics to treatment in urban slum settings. Objective The objective of our study is to develop and evaluate the AI-guided citizen-centric platform that will support the uptake of maternal health services among pregnant women seeking antenatal care living in urban slum settings. Methods We will conduct a cross-sectional study using a mixed methods approach to enroll 225 pregnant women aged 18-44 years, living in the urban slums of Delhi for more than 6 months, seeking antenatal care, and who have smartphones. Quantitative and qualitative data will be collected using an Open Data Kit Android-based tool. Variables gathered will include sociodemographics, clinical history, pregnancy history, dietary history, COVID-19 history, health care facility data, socioeconomic status, and pregnancy outcomes. All data gathered will be aggregated into a common database. We will use AI to predict the early at-risk pregnancy outcomes (in terms of the type of delivery method, term, and related complications) depending on the needs of the beneficiaries translating into effective service-delivery improvements in enhancing the use of maternal health services among pregnant women seeking antenatal care. The proposed research will help policy makers to prioritize resource planning, resource allocation, and the development of programs and policies to enhance maternal health outcomes. The academic research study has received ethical approval from the University Research Ethics Committee of Dehradun Institute of Technology (DIT) University, Dehradun, India. Results The study was approved by the University Research Ethics Committee of DIT University, Dehradun, on July 4, 2021. Enrollment of the eligible participants will begin by April 2022 followed by the development of the predictive model by October 2022 till January 2023. The proposed AI-guided citizen-centric tool will be designed, developed, implemented, and evaluated using principles of human-centered design that will help to predict early at-risk pregnancy outcomes. Conclusions The proposed internet-enabled AI-guided prediction model will help identify the potential risk associated with pregnancies and enhance the uptake of maternal health services among those seeking antenatal care for safer deliveries. We will explore the scalability of the proposed platform up to different geographic locations for adoption for similar and other health conditions. International Registered Report Identifier (IRRID) PRR1-10.2196/35452

BACKGROUND Pregnant women are considered to be a “high risk” group with limited access to health facilities in urban slums. Barriers to utilization of health services may lead to maternal and child mortality, morbidity, low birth weight, and children with stunted growth. Application of artificial intelligence (AI) can provide substantial improvements in all areas of healthcare from diagnostics to treatment. There have been several technological advances within the field of AI, however, AI not merely driven by what is technically feasible, but by what is humanly desirable is the need of the hour. OBJECTIVE The objective of our study is to develop and evaluate the AI guided citizen centric platform to enhance the uptake of maternal health services (antenatal care) amongst the pregnant women living in urban slum settings. METHODS A cross-sectional mixed method approach employed to collect data among pregnant women, aged 18-44 years, living in urban slums of South Delhi. A convenience sampling used to recruit 225 participants at the Anganwadi centres (AWC) after obtaining consent from the eligible participants. Inclusion criteria includes pregnant individuals residing in urban slums for more than 3 months, having smartphones, visiting AWC for seeking antenatal care. Quantitative and qualitative data will be collected electronically using Open Data Kit (ODK) based opensource tool from eligible participants. Data will be collected on clinical as well as socio-demographic parameters (based on existing literature). We aim to develop an innovative AI guided citizen centric decision support platform to effectively manage pregnancy and its outcomes among urban poor populations. The proposed research will help policymakers to prioritize resource planning, resource allocation and development of programs and policies to enhance maternal health outcomes. RESULTS The AI guided citizen centric decision support platform will be designed, developed, implemented and evaluated using principles of human centred design and findings of the study will be reported to diverse stakeholders. The tested and revised platform will be deployed for use across various stakeholders such as pregnant women, healthcare professionals, frontline workers, and policymakers. CONCLUSIONS With the understanding, use and adoption of emerging and innovative technologies such as AI, maternal health informatics can be at the forefront to help pregnant women in crisis. The proposed platform will potentially be scaled up to different geographic locations for adoption for similar and other health conditions.

BACKGROUND Diabetes represents an important public health challenge in India and Globally. It affects quality of life and is one of the leading causes of death and disability. The burden on global health is huge and about 463 million adults are currently living with diabetes. 77 million people in India in the age group of 20-79 years are affected by this pandemic and total cost to health expenditure is 8 billion US dollars, therefore huge burden, and great economic cost on Public health. The self-management of diabetes, the research priorities include exploring the concept of diabetes self-management and major research questions would comprise of asking what affects self-management in persons with diabetes and how do m-health application and interventions can impact on the self-management behaviors in development, utility of the m-health app in self-management of person with diabetes. Therefore, this project research is of great significance and would bring an integrative approach on self-care management OBJECTIVE To design, develop and evaluate the impact of m-health enabled nutrition informatics intervention for home based self-management of type 2 diabetes in an Indian setting. METHODS A mixed research study will be conducted between January 2022 and January 2023. A sample of approximately 250 individuals will be recruited and enrolled using a nonprobability complete enumeration sampling method from selected urban settings of Delhi inclusion and exclusion criteria with age20-79 years male and female with Type 2 diabetes and have access to Smart phone Data will be collected using which questionnaires. The collected data will be used to assess use and utility of mobile health application developed. The knowledge, attitudes, practices, and beliefs regarding Diabetes self-care management. Lastly, the study questionnaire system usability survey(SUS) will be used to assess the usability of mobile applications on selfcare management of Diabetes RESULTS A pilot of 250 individuals has been conducted to pretest the DBMS questionnaire. The data collection will be initiated from January 2022, and the initial results are planned for publication by October 2022.Descriptive analysis of the gathered data will be performed using SPSS V11, and reporting of the results will be done at 95% CIs and P=.0.05. CONCLUSIONS The findings of the study would inform the elements essential for the development of m-health intervention to improve self-care management of diabetes at home settings. The usefulness and acceptance of the proposed intervention will be conducted. CLINICALTRIAL DITU/UREC/2021/07/10