Information Package / Course Catalogue
Artificial Intelligence in Women's Health Nursing Practices
Course Code: HDK639
Course Type: Area Elective
Couse Group: Third Cycle (Doctorate Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 4
Objectives of the Course

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.

Course Content

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.

Name of Lecturer(s)
Learning Outcomes
1.To be able to understand how the health data ecosystem works in the context of artificial intelligence and machine learning.
2.To be able to explain indicators specific to women's health in electronic health records and to evaluate data privacy principles.
3.To be able to interpret the contributions of artificial intelligence applications used in fertility and cycle monitoring to reproductive health.
4.To be able to explain the operation and usage areas of remote monitoring systems in obstetric follow-ups.
5.To be able to evaluate the effects of wearable technologies and IoT-based systems on women's health
6.To be able to explain the potential effects of artificial intelligence and machine learning-based gamification applications on women's health.
7.To be able to define how large language models and chatbots can be used in women's health nursing education.
8.To be able to evaluate the contributions of artificial intelligence applications to care processes in perinatal and postpartum care.
9.To be able to explain the basic features of artificial intelligence-supported health services in the climacteric period.
10.To be able to understand the usability of artificial intelligence-based solutions in the management of urogynecological problems.
11.To be able to explain the place and importance of artificial intelligence applications in women's health screenings.
12.To be able to interpret the care and monitoring processes carried out with artificial intelligence in gynecological oncology.
13.To be able to discuss the ethical dimensions of artificial intelligence-based care applications in the field of women's health.
14.To be able to understand the future trends in artificial intelligence and machine learning in women's health nursing.
Recommended or Required Reading
1.Ahn, K. H., & Lee, K. S. (2022). Artificial İntelligence İn Obstetrics. Obstetrics & Gynecology Science, 65(2), 113-124.
2.Artificial Intelligence And Machine Learning For Women's Health Issues. (2024). Hollanda: Academic Press.Deep Learning İn Breast Cancer Imaging (2024)
3.Arun, Raj. Mastering Large Language Models with Python: Unleash the Power of Advanced Natural Language Processing for Enterprise Innovation and Efficiency Using Large Language Models (LLMs) with Python. Hindistan, Orange Education Pvt Limited, 2024.
4.Caelen, Olivier, and Blete, Marie-Alice. Developing Apps with GPT-4 and ChatGPT. Amerika Birleşik Devletleri, O'Reilly Media, 2023.
5.Clancy, T. R. (2020). Artificial İntelligence And Nursing: The Future İs Now. JONA: The Journal Of Nursing Administration, 50(3), 125-127.
6.Edmonds, J. K. (2023). Use Of Artificial Intelligence To Improve Women’s Health And Enhance Nursing Care. Journal Of Obstetric, Gynecologic & Neonatal Nursing, 52(3), 169-171.
7.Esen, A. C., & Öter, E. G. (2023). Jinekolojik Operasyonların Hasta Yönetiminde Dijital Teknolojilerin ve Yapay Zekanın Kullanımı. In International Conference On Frontiers İn Academic Research (Vol. 1, Pp. 499-505).
8.Esen, A. C., & Öter, E. G. (2023). Yapay Zekâ ve Hemşirelik. Sağlık & Bilim 2023: Hemşirelik-Iıı, 7.
9.Esen, A. C., & Öter, E. G. (2025). Gebelik Takibi ve Yönetiminde Yenilikçi Yaklaşım Olarak Yapay Zekâ Kullanımı: Bir Literatür Derlemesi, III. Uluslararası ve IV. Ulusal Kadın Sağlığı Hemşireliği Kongresi.
10.Jeong, G. H. (2020). Artificial İntelligence, Machine Learning, And Deep Learning İn Women’s Health Nursing. Korean Journal Of Women Health Nursing, 26(1), 5-9.
11.O'Connor, S., Yan, Y., Thilo, F. J., Felzmann, H., Dowding, D., & Lee, J. J. (2023). Artificial İntelligence İn Nursing And Midwifery: A Systematic Review. Journal Of Clinical Nursing, 32(13-14), 2951-2968
12.Predicting Pregnancy Complications Through Artificial Intelligence And Machine Learning. (2023). Amerika Birleşik Devletleri: IGI Global.
13.Robert, N. (2019). How Artificial İntelligence İs Changing Nursing. Nursing Management, 50(9), 30-39.
14.Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. pearson.
15.Sarno, L., Neola, D., Carbone, L., Saccone, G., Carlea, A., Miceli, M., ... & Maruotti, G. M. (2023). Use Of Artificial İntelligence İn Obstetrics: Not Quite Ready For Prime Time. American Journal Of Obstetrics & Gynecology MFM, 5(2), 100792.
16.Sezgin E, Chekeni F, Lee J, Keim S. Clinical Accuracy Of Large Language Models And Google Search Responses To Postpartum Depression Questions: Cross-Sectional Study. J Med Internet Res. 2023 Sep 11;25:E49240. Doi: 10.2196/49240. PMID: 37695668; PMCID: PMC10520763.
17.Utilizing AI Techniques For The Perimenopause To Menopause Transition. (2024). Amerika Birleşik Devletleri: IGI Global.Adoption Barriers & Facilitators Of Wearable Health Devices With AI (2025)
Weekly Detailed Course Contents
Week 1 - Theoretical
Specific to AI and Machine Learning from the Health Data Ecosystem and Data Types
Week 2 - Theoretical
Intra-Hospital Integration: Women's Health Indicators, Data Quality, Anonymization and Privacy in Electronic Health Records
Week 3 - Theoretical
AI and Machine Learning in Reproductive Health: Fertility and Cycle Tracking
Week 4 - Theoretical
Remote Monitoring with AI and Machine Learning in Obstetric Monitoring
Week 5 - Theoretical
AI, Machine Learning and Wearable Technologies: Effects of IoT on Women's Health
Week 6 - Intermediate Exam
Midterm Exam / AI and Machine Learning Based Gamification and Women's Health Applications
Week 7 - Theoretical
Large Language Models (LLM) / Women's Health Education with Chatbots
Week 8 - Theoretical
Use of AI and Machine Learning in Perinatal and Postpartum Care
Week 9 - Theoretical
AI and Machine Learning in Care and Management of Climacteric Period
Week 10 - Theoretical
Use of AI and Machine Learning in Care Management of Urogynecological Problems
Week 11 - Theoretical
AI and Machine Learning in Women's Health Screenings
Week 12 - Theoretical
AI and Machine Learning in Gynecologic Oncology Care Management and Monitoring Use of Intelligence and Machine Learning
Week 13 - Theoretical
Ethical Dimensions of AI and Machine Learning Based Patient Care in Women's Health Practices
Week 14 - Final Exam
Final Exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Presentation1%5
Midterm Examination1%20
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory144284
Assignment2204
Midterm Examination1415
Final Examination1617
TOTAL WORKLOAD (hours)100
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
OÇ-1
4
4
4
4
3
5
OÇ-2
4
4
3
4
4
3
OÇ-3
5
5
3
5
5
4
OÇ-4
5
4
5
4
4
5
OÇ-5
4
3
3
4
4
3
OÇ-6
4
3
3
4
5
3
OÇ-7
4
5
5
4
5
4
OÇ-8
5
4
5
4
5
4
OÇ-9
5
3
5
4
4
4
OÇ-10
4
5
5
4
4
4
OÇ-11
5
5
4
4
5
5
OÇ-12
4
4
4
4
5
5
OÇ-13
5
4
5
3
5
5
OÇ-14
4
3
4
3
3
4
Adnan Menderes University - Information Package / Course Catalogue
2026