Information Package / Course Catalogue
| Course Code | : HDK548 |
| Course Type | : Area Elective |
| Couse Group | : Second Cycle (Master's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 2 |
| Prt. | : 0 |
| Credit | : 2 |
| Lab | : 0 |
| ECTS | : 4 |
To train expert nurses who can recognize artificial intelligence and machine learning applications in women's health nursing at a basic level, distinguish the areas of use of these technologies according to women's life stages, take basic ethical principles into consideration in data-based care processes and produce solutions with interdisciplinary approaches.
The course introduces artificial intelligence and machine learning applications in different life stages related to women's health (such as reproductive age, pregnancy, birth, postpartum, climacterium). Data collection and evaluation of these data with tools such as electronic health records, wearable technologies, mobile health applications are discussed according to women's health needs in different periods. Ethical aspects of artificial intelligence-supported applications, usage examples, and their effects on women's health nursing practices are discussed.
| 1. | To be able to define health data types and basic areas of use of artificial intelligence and machine learning in women's health. |
| 2. | To be able to explain data components related to women's health in electronic health records and data privacy principles. |
| 3. | To be able to list artificial intelligence applications used in reproductive health and the areas they contribute |
| 4. | To be able to list the basic features of remote monitoring systems used in obstetric monitoring. |
| 5. | To be able to define the functions of wearable technologies in women's health monitoring. |
| 6. | To be able to explain the basic aspects of gamification-based artificial intelligence applications used in women's health education |
| 7. | To be able to outline the roles of large language models and chatbots in women's health counseling. |
| 8. | To be able to recognize artificial intelligence-supported care applications used in the perinatal and postpartum period. |
| 9. | To be able to recognize artificial intelligence-supported care applications used in the perinatal and postpartum period. |
| 10. | o be able to list the basic components of mobile health applications for the climacteric period. |
| 11. | Be able to define artificial intelligence-supported women's health screening programs and their functioning. |
| 12. | Be able to compare artificial intelligence-supported monitoring and care models in gynecological oncology. |
| 13. | Be able to discuss the ethical and privacy aspects of artificial intelligence applications in women's health services. |
| 14. | Be able to explain future trends in artificial intelligence technologies in women's health nursing. |
| 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, 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) |
| Type of Assessment | Count | Percent |
|---|---|---|
| Attending Lectures | 1 | %5 |
| Presentation | 1 | %5 |
| Midterm Examination | 1 | %20 |
| Final Examination | 1 | %70 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 2 | 2 | 56 |
| Presentation | 2 | 8 | 2 | 20 |
| Midterm Examination | 1 | 11 | 1 | 12 |
| Final Examination | 1 | 11 | 1 | 12 |
| TOTAL WORKLOAD (hours) | 100 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | PÇ-12 | |
OÇ-1 | 4 | 3 | 4 | 4 | 3 | 3 | 3 | 3 | 3 | 3 | 4 | 4 |
OÇ-2 | 4 | 3 | 4 | 4 | 4 | 3 | 3 | 3 | 3 | 3 | 4 | 4 |
OÇ-3 | ||||||||||||
OÇ-4 | 5 | 5 | 5 | 5 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 5 |
OÇ-5 | 5 | 5 | 5 | 5 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 5 |
OÇ-6 | 5 | 4 | 5 | 4 | 5 | 3 | 4 | 5 | 3 | 4 | 4 | 5 |
OÇ-7 | 5 | 5 | 4 | 5 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 5 |
OÇ-8 | 5 | 5 | 4 | 5 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 5 |
OÇ-9 | 5 | 5 | 4 | 5 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 5 |
OÇ-10 | 5 | 5 | 4 | 5 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 5 |
OÇ-11 | 5 | 5 | 4 | 5 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 5 |
OÇ-12 | 5 | 5 | 4 | 5 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 5 |
OÇ-13 | 5 | 5 | 4 | 5 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 5 |
OÇ-14 | 5 | 4 | 4 | 5 | 3 | 5 | 4 | 4 | 3 | 4 | 3 | 4 |