ZAMONAVIY BIOTIBBIYOTDA TIBBIY TERMINOLOGIYANING TIZIMLASHTIRISH VA SUN’IY INTELLEKT YORDAMIDA STANDARTLASHTIRISHNING AHAMIYATI
Kalit so‘zlar:
tibbiy terminologiya; standartlashtirish; tizimlashtirish; sun’iy intellekt; SNOMED CT; Medline; ChatGPT; biotibbiyotAbstrak
Ushbu maqolada zamonaviy biotibbiyot sohasida tibbiy terminologiyani tizimlashtirish
zarurati va uni sun’iy intellekt yordamida standartlashtirish masalalari yoritilgan. Tadqiqotda
tibbiy axborotlar integratsiyasi va ma’lumotlar almashinuvida yagona terminologiya tizimi muhim
ahamiyatga ega ekani ta’kidlanadi. SNOMED CT, ICD va MeSH kabi xalqaro standart
terminologiya bazalari tibbiy tushunchalarni bir xilda ifodalash uchun zamin yaratadi. Sun’iy
intellekt texnologiyalari, xususan ChatGPT kabi yirik til modellari, tibbiy matnlardagi
terminologiyani avtomatik normallashtirishda qo‘llanilishi mumkin. Maqolada ChatGPT va
Medline/PubMed ma’lumotlar bazalari misolida tibbiy atamalarni standartlashtirish usullari
ko‘rib chiqiladi. Natijalarga ko‘ra, sun’iy intellekt yordamida terminologiyani birxillashtirish
klinik axborot tizimlari o‘rtasidagi o‘zaro bog‘liqlikni yaxshilashini hamda ilmiy izlanishlar
samaradorligini oshiradi.
References
Park, H.-A. (2024). Why Terminology Standards Matter for Data-driven Artificial Intelligence in Healthcare. Annals of Laboratory Medicine, 44(6), 467–471. https://doi.org/10.3343/alm.2024.0105:contentReference[oaicite:36]{index=36}
;:contentReference[oaicite:37]{index=37}
SNOMED International. (2025). What is SNOMED CT? Retrieved April 3, 2025, from https://www.snomed.org/what-is-snomedct:contentReference[oaicite:44]{index=44}:contentReference[oaicite:45]{index=45}
Oztermeli, A. D. (2025). Is ChatGPT a reliable tool for explaining medical terms? Cureus, 17(1), e77258. https://doi.org/10.7759/cureus.77258:contentReference[oaicite:41]{index=41}:contentReference[oaicite:42]{index=42}
Kocaman, V. (2023, April 20). Comparing Spark NLP for Healthcare and ChatGPT in extracting ICD10-CM codes from clinical notes [Blog post]. John Snow Labs. Retrieved from https://www.johnsnowlabs.com/comparing-spark-nlp-for-healthcare-and-chatgpt-in-extractingicd10-cm-codes-from-clinical-notes/:contentReference[oaicite:43]{index=43}
Yoon, D., Han, C., Kim, D. W., Kim, S., Bae, S., Ryu, J. A., & Choi, Y. (2024). Redefining health care data interoperability: Empirical exploration of large language models in information exchange. Journal of Medical Internet Research, 26(1), e56614.
https://doi.org/10.2196/56614:contentReference[oaicite:38]{index=38}
Berkowitz, J. S., Srinivasan, A., Acitores Cortina, J. M., Fatapour, Y., & Tatonetti, N. (2025). Biomedical text normalization through generative modeling. medRxiv (preprint). https://doi.org/10.1101/2024.09.30.24314663:contentReference[oaicite:39]{index=39}:contentReference[oaicite:40]{index=40}
Bodenreider, O. (2004). The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Research, 32(suppl_1), D267–D270. https://doi.org/10.1093/nar/gkh061:contentReference[oaicite:46]{index=46}
National Library of Medicine. (2021). PubMed®: A brief introduction [Fact sheet]. Bethesda, MD: U.S. National Institutes of Health. PubMed comprises more than 33 million citations for biomedical literature from MEDLINE, life science journals, and online books.
Downloads
Nashr qilingan
Nashr
Bo'lim
License
Copyright (c) 2025 Nasirdinova Yorkinoy Abdumuxtarovna

This work is licensed under a Creative Commons Attribution 4.0 International License.