GINIKOLOGIYADA AXBOROT TEXNALOGIYALARI VA SUN’IY INTELEKTDAN FOYDALANISH: AYOLLAR REPRODUKTIV SALOMATLIGINI DIAGNOSTIKA VA DAVOLASHDA RAQAMLI INOVATSIYALAR
Keywords:
sun’iy intelekt,raqamli tibbiyot,ginikologiya,reproduktiv salomatlik,diagnostika,telemeditsina, big data,neyron tarmoqlar, instrumental tahlil, UZI, MRT, kilinik qarorlarni qo’llab quvvatlash, mobil ilovalar.Abstract
Ushbu maqolada zamonaviy ginekologiya sohasida axborot texnologiyalari va sun’iy intellekt (SI)dan foydalanishning asosiy tamoyillari, yo‘nalishlari va klinik ahamiyati batafsil bayon etilgan. Raqamli tibbiyotning jadal rivojlanishi ayollar reproduktiv salomatligini erta diagnostika qilish, davolash jarayonlarini optimallashtirish bilan birga uzoq muddatli klinik monitoringni tashkil etishda muhim omilga aylanmoqda.
Sun’iy intellekt algoritmlari yordamida katta hajmdagi klinik, laborator hamda instrumental tekshiruv ma’lumotlarini tahlil qilish, ginekologik kasalliklarning yashirin bosqichlarini aniqlash, onkologik o‘zgarishlarni erta prognozlash hamda individual davolash strategiyalarini ishlab chiqish imkoniyati kengaymoqda. UZI, MRT va KT tasvirlarini neyron tarmoqlar orqali qayta ishlash tashxis sifatini yanada oshiradi.
References
1. World Health Organization (WHO). Digital health: transforming and extending the delivery of health services. WHO Report, 2023.
2. ACOG – American College of Obstetricians and Gynecologists. Artificial Intelligence in Obstetrics and Gynecology: Clinical Applications and Challenges. ACOG Committee Opinion, 2022.
3. Chen, L., Li, X., & Wang, Q. Artificial Intelligence Applications in Reproductive Medicine. Human Reproduction Update, 2021.
4. Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. Big Data in Healthcare: Management, Analysis and Future Prospects. Journal of Big Data, 2019.
5. Esteva, A., Robicquet, A., Ramsundar, B., et al. A guide to deep learning in healthcare. Nature Medicine, 2019.
6. Huang, X., Deng, C., & Zhang, Y. Application of Machine Learning in Early Diagnosis of Endometriosis. Reproductive Biology and Endocrinology, 2022.
7. Minz, A., Cai, Y., & Zhang, T. AI-assisted Ultrasound Imaging for Fetal Health Assessment. IEEE Transactions on Medical Imaging, 2020.
8. Razzak, M. I., Naz, S., & Zaib, A. Deep Learning for Medical Image Processing: Overview, Challenges and Future. Neural Computing and Applications, 2018.
9. Тухтаев, Х. Ш., & Хакимова, Г. Tibbiyotda axborot texnologiyalari. Toshkent: TMA nashriyoti, 2021.
10. Karimov, A., & Rasulova, N. Sun’iy intellekt va tibbiyot innovatsiyalari. Toshkent: Innovatsiyalar Agentligi, 2022.
11. Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. AI in Healthcare: Past, Present and Future. NEJM AI Review, 2023.
12. FIGO (International Federation of Gynecology and Obstetrics). Digital Tools in Global Women's Health. FIGO Guidelines, 2022.
