Keywords
Abstract
Бутун дунё бўйича операциядан кейинги асоратлар: госпитализация муддатининг узайиши, соғлиқни сақлаш харажатларининг ошиши ва ўлим ҳолатлари юқорилигининг асосий сабаби бўлиб қолмоқда. Сунъий интеллект (СИ) соҳасидаги сўнгги ютуқлар анъанавий клиник баҳолаш тизимларига нисбатан хавфларни башорат қилишни такомиллаштириш мақсадида мураккаб периоператив маълумотларни интеграция қила оладиган машинавий ўрганиш (МО) ва чуқур ўрганиш (ЧЎ) моделларини ишлаб чиқишга имкон беради.
Замонавий технологиялар ва суний интеллектнинг жарроҳлик соҳасида қўлланилишига қаратилган илмий ишлар фақат маълум жарроҳлик усуллари, маълум қисмдаги муайян муаммолар ёки сунъий интеллектнинг ўзига хос ёндашувларига эътибор қаратади, бу эса уларнинг умумий жарроҳликдаги қўлланилишига, периоператив прогноз қилиш имкониятларини чекланишига сабаб бўлади. Умумий жарроҳликда операциядан кейинги асоратларни башорат қилиш учун сунъий интеллектга асосланган моделлар бўйича мавжуд маълумотларни объектив баҳолаш, сўнгги янгиликларни умумий жарроҳлик соҳасига тўлиқ тадбиқ қилиш олдимизда турган кейинги босқич муаммолари ҳисобланади.
References
1. Bihorac A, Ozrazgat-Baslanti T, Ebadi A, Motaei A, Madkour M, Pardalos PM, et al. MySurgeryRisk: Development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg. 2019;269(4):652-662.
2. Bilimoria KY, Liu Y, Paruch JL, Zhou L, Kmiecik TE, Ko CY, et al. Development and evaluation of the universal ACS NSQIP Surgical Risk Calculator. J Am Coll Surg. 2013;217(5):833- 842.
3. Bronnert R, Besch G, Hild O, et al. Performance of artificial intelligence models for predicting intraoperative complications during surgery in real time: A systematic review and metaanalysis protocol. BMJ Open. 2025;15:e106204.
4. Chung P, Fong CT, Walters AM, Aghaeepour N, Yetisgen M, O'Reilly-Shah VN. Large language model capabilities in perioperative risk prediction and prognostication. JAMA Surg. 2024;159(8):928-937.
5. Collins GS, Dhiman P, Navarro CLA, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of the TRIPOD-AI and PROBAST-AI reporting and risk of bias tools for studies developing or validating artificial intelligence prediction models. BMJ Open. 2021;11:e048008.
6. Fuentes SMS, Chávez LAF, López EMM, Cardona CDC, Goti LLM. The impact of artificial intelligence in general surgery: Enhancing precision, efficiency, and outcomes. Int J Res Med Sci. 2025;13(1):293-297.
7. Hassan Mukhtar M, Ahmed AUB, Mohammed MAS, Alsubhi AF, Aljuaid SH, Almutairi AK, et al. The role of artificial intelligence in the prediction of bariatric surgery complications: A systematic review. Cureus. 2025;17(4):e82461.
8. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst.2017;30:4765-4774.
9. Mathis MR, Engoren MC, Williams AM, Biesterveld BE, Croteau AJ, Cai L, et al. Prediction of postoperative deterioration in cardiac surgery patients using electronic health record and physiologic waveform data. Anesthesiology. 2022;137(5):586-601.
10. Moll V, Khanna AK, Mathur P. Artificial intelligence for the prediction of postoperative complications in the critically ill. Crit Care Sci. 2025;37:e20250025.
11. Nepogodiev D, Martin J, Biccard B, Makupe A, Bhangu A; GlobalSurg Collaborative. Global burden of postoperative death. Lancet. 2019;393(10170):401.
12. Russell SJ, Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. Pearson; 2021.
13. Spence J, LeManach Y, Chan MTV, Wang CY, Sigamani A, Xavier D, et al. Association between complications and death within 30 days after noncardiac surgery. CMAJ. 2019;191(30):E830-E837
14. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
15. van de Sande D, van Genderen ME, Huiskens J, Gommers D, van Bommel J. Moving from bytes to bedside: A systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med. 2021;47(7):750-760.