To train doctoral level experts who can define data types related to artificial intelligence (AI) and machine learning (ML) applications in women's health nursing; analyze data obtained from electronic health records, wearable technologies and remote monitoring systems in the health data ecosystem; critically evaluate AI/ML-based applications specific to women's health; and integrate these technologies into holistic care processes with an ethical, patient-centered and interdisciplinary perspective.

The course examines artificial intelligence (AI) and machine learning (ML) applications in the context of women's health nursing from a health data ecosystem perspective; evaluates women's health indicators, data quality, anonymization, and privacy issues in electronic health records. Applications in various areas, from fertility and cycle tracking to remote monitoring in obstetric follow-ups, from wearable technologies to IoT-based solutions, are examined. In addition, gamification-based health applications, education processes with large language models (LLMs) and chatbots, perinatal and postpartum care, climacteric period management, urogynecological problems, screening programs, and AI/ML-supported approaches are discussed within the scope of gynecological oncology follow-ups. The course comprehensively addresses the effects of AI-based patient care on women's health and future trends in nursing, along with ethical considerations.