Tibbiyotda sun’iy intellektni rivojlantirishda mahalliy tibbiy ma’lumotlarning ahamiyati: TAMES AI loyihasi asosida
The importance of local medical data for developing artificial intelligence in medicine: the case of the TAMES AI project
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Abstract
Although artificial intelligence (AI) systems for medical diagnostics have demonstrated high performance under controlled research conditions, their implementation in diverse clinical settings remains problematic owing to dataset shift, population heterogeneity and insufficient local validation. Thyroid nodule assessment is a clear example: diagnostic algorithms trained mainly on data from North American and European populations may often fail to deliver the expected performance when applied in regions that differ in iodine status, genetic characteristics and healthcare infrastructure, such as Central Asia. Using the TAMES AI (Thyroid-AI Malignancy Evaluation System) project as a case, this article analyzes the methodological and strategic importance of multimodal clinical data from local sources for the development of medical artificial intelligence systems.
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Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. DOI: https://doi.org/10.3322/caac.21492
Cibas ES, Ali SZ. The Bethesda System for Reporting Thyroid Cytopathology. Thyroid. 2009;19(11):1159–1165. DOI: https://doi.org/10.1089/thy.2009.0274
Ferlay J, Colombet M, Soerjomataram I, et al. Global and Regional Incidence, Mortality and Prevalence of Cancers. Lyon, France: International Agency for Research on Cancer; 2018.
Guth S, Theune U, Aberle J, Galach A, Bamberger CM. Very high prevalence of thyroid nodules detected by high-frequency (13 MHz) ultrasound examination. Eur J Clin Invest. 2009;39(8):699–706. DOI: https://doi.org/10.1111/j.1365-2362.2009.02162.x
World Health Organization. Guideline: Fortification of Food-Grade Salt with Iodine for the Prevention and Control of Iodine Deficiency Disorders. Geneva: WHO; 2014.
Tessler FN, Middleton WD, Grant EG, et al. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): white paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017;14(5):587–595.
Hegedüs L, Paschke R, Krohn K, Bonnema SJ. European Thyroid Association guidelines for thyroid nodule management. Eur Thyroid J. 2018;7(3):138–149.
Tan GH, Gharib H. Thyroid incidentalomas: management approaches to nonpalpable nodules discovered incidentally on thyroid imaging. Ann Intern Med. 1997;126(3):226–231. DOI: https://doi.org/10.7326/0003-4819-126-3-199702010-00009
Tessler FN, Middleton WD, Grant EG, et al. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): white paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017;14(5):587–595. DOI: https://doi.org/10.1016/j.jacr.2017.01.046
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56. DOI: https://doi.org/10.1038/s41591-018-0300-7
World Health Organization. Guideline: Fortification of Food-Grade Salt with Iodine for the Prevention and Control of Iodine Deficiency Disorders. Geneva: WHO; 2014.
World Health Organization. Ethics and Governance of Artificial Intelligence for Health. Geneva: WHO; 2021.
Yoon JH, Moon HJ, Kim EK, Kwak JY. Inadequate cytology in thyroid fine-needle aspiration biopsy. Cytopathology. 2011;22(4):245–251.