SUN'IY INTELLEKTNING SOG'LIQNI SAQLASHGA TA'SIRI
Keywords:
Sun'iy intellekt (SI), sog'liqni saqlash texnologiyalari, diagnostika vositalari, shaxsiylashtirilgan tibbiyot,bashoratli tahlil, jarrohlikda SIAbstract
Sun'iy intellekt (SI) diagnostika aniqligini oshirish, davolash natijalarini yaxshilash, operatsion xarajatlarni kamaytirish va sog'liqni saqlash xizmatlaridan foydalanish imkoniyatini oshirish orqali sog'liqni saqlash sohasini jadal o'zgartirmoqda. Ushbu maqola SI-ning sog'liqni saqlashda turli xil qo'llanilishini, shu jumladan diagnostika vositalari, shaxsiy tibbiyot, bemorlarni monitoring qilish va robot jarrohliklarini o'rganadi. Maqolada, shuningdek, SI-ning samaradorlikni oshirish, xarajatlarni pasaytirish va qulaylikni oshirish kabi muhim afzalliklari muhokama qilinadi, shu bilan birga ma'lumotlar maxfiyligi masalalari, axloqiy masalalar va SI-ni mavjud sog'liqni saqlash tizimlariga integratsiyalashuvi kabi asosiy muammolarni hal qiladi. SI texnologiyasi rivojlanishda davom etar ekan, uning butun dunyo bo'ylab sog'liqni saqlash tizimlarida inqilob qilish salohiyati juda katta, ammo uni muvaffaqiyatli va axloqiy amalga oshirishni ta'minlash uchun jiddiy to'siqlarni engish kerak. Maqola SI-ga asoslangan sog'liqni saqlash innovatsiyalarining
kelajakdagi tendentsiyalarini, shu jumladan SIning dori-darmonlarni kashf etish, bashoratli tibbiyot va ruhiy salomatlikni saqlashdagi ro’lini ta'kidlab o'tadi.
References
1. Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make
Healthcare Human Again. Basic Books.
2. Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level
classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
3. Obermeyer, Z., Powers, B. W., Vogeli, C., & Mullainathan, S. (2019).
Dissecting racial bias in an algorithm used to manage the health of populations.
Science, 366(6464), 447-453.
4. Choi, E., Bahadori, M. T., Schuetz, A., et al. (2016). Doctor AI: Predicting
clinical events via recurrent neural networks. Journal of Machine Learning Research,
17(1), 1-16.
5. Jiang, F., Jiang, Y., Zhi, H., et al. (2017). Artificial intelligence in healthcare:
Past, present, and future. Seminars in Cancer Biology, 54, 1-11.
6. Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in
healthcare. Future Healthcare Journal, 6(2), 94-98.7. Rajpurkar, P., Irvin, J., Zin, L., et al. (2017). Deep learning for chest radiograph
diagnosis: A retrospective comparison of the CheXNet algorithm to practicing
radiologists. PLOS Medicine, 14(11), e1002686.
8. Liu, Y., Chen, P. C., Krause, J., & Peng, L. (2019). How AI can enhance patient
care and reduce errors. Journal of the American Medical Association (JAMA), 322(4),
332-333.
9. Vincent, K., & Sampson, R. (2020). AI and the future of healthcare: Enhancing
diagnosis and patient outcomes. The Lancet Digital Health, 2(3), e141-e150.
