Smart Normal Labor: Healthcare Providers' Experience With an AI-Based Mobile App

Pregnancy and childbirth are uniquely important events in women's lives because they are accompanied by major physical, emotional, and psychological changes. Maternal satisfaction, emotional well-being, and perceptions of childbirth are strongly influenced by the quality of labor management. A woman's childbirth experience is shaped by multiple factors, including communication, autonomy, and active participation in the decision-making process. These factors are widely recognized as important indicators of the quality of maternity care. \[1\] Recent demographic changes and global population growth have placed increasing demands on healthcare systems, particularly maternal health services. High birth rates in some regions, combined with shortages of trained healthcare professionals, have created a need for scalable, adaptable, and innovative models of care. In response to these challenges, digital health technologies have emerged as promising tools to enhance the quality of maternity care and support both healthcare providers and pregnant women. \[2\]

Inclusion Criteria Participants must meet the following conditions to be included in the study: 1. Healthcare providers (obstetricians and nurses) currently working in the Labor Kiosk, Obstetrics and Gynecology Department, or Outpatient Gynecology Clinics at Mansoura University Hospital. 2. Direct involvement in the care and supervision of women in active labor. 3. For the intervention group: previous exposure to and use of the AI-based smart normal labor application for a minimum defined period (e.g., 1 month). 4. For the control group: no prior use of the AI-based application, following standard care practices. 5. Willingness to participate and provide informed consent. Exclusion Criteria Participants will be excluded if they: 1. Are healthcare providers not directly involved in labor management (e.g., administrative staff or laboratory personnel). 2. Have less than the minimum required clinical experience in labor management (e.g., \