Artificial intelligence in endocrinology: Breakthrough technologies and prospects
https://doi.org/10.14341/omet13238
Abstract
In today’s world, characterized by the growing prevalence of endocrine diseases and the complexity of their diagnosis and treatment, AI offers unique opportunities to improve medical care. In the article, we analyze how AI algorithms help detect and classify pathological changes in ultrasound, MRI, CT providing endocrinologists with additional tools for fast and accurate diagnosis. In addition, we are considering the use of AI for big data analysis, including electronic medical records (EHRs), which allows us to develop predictive models and personalize treatment. Special attention is paid to the role of AI in monitoring patients with chronic endocrine diseases, including continuous monitoring of blood glucose levels in diabetes mellitus
This article will be useful for endocrinologists, researchers, students and anyone interested in the use of artificial intelligence in modern medicine.
About the Authors
A. P. Pershina-MiliutinaRussian Federation
Anastasia P. Pershina-Miliutina
Moscow
Competing Interests:
none
M. A. Telegina
Russian Federation
Maria А. Telegina - ResearcherID: JMB-6130-2023.
Mir Avenue, Building 102, Block 23
Competing Interests:
none
E. V. Еrshova
Russian Federation
Ekaterina V. Ershova - MD, PhD
Moscow
Competing Interests:
none
K. A. Komshilova
Russian Federation
Ksenia A. Komshilova - MD, PhD.
Moscow
Competing Interests:
none
P. A. Еrshova
Russian Federation
Polina A. Ershova
Moscow
Competing Interests:
none
References
1. Abstract of the dissertation of A.R. Elfimova for the degree of Candidate of Medical Sciences on the topic «Prediction of adverse events after parathyroidectomy in patients with primary hyperparathyroidism using mathematical modeling methods» To quote from: https://www.endocrincentr.ru/sites/default/files/specialists/science/dissertation/avtoreferat_elfimova.pdf
2. Zhu Y, Zhang Y, Yang M, et al. Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study. Diabetes Metab Syndr Obes. 2024;17:1987-1997. doi: https://doi.org/10.2147/DMSO.S458263
3. Vikulova O. K., Elfimova A. R., Zheleznyakova A.V. and others. Risk calculator for chronic kidney disease: new possibilities for predicting pathology in patients with diabetes mellitus. — 2022. — Issue 24. — No. 4. — pp. 224-233
4. Calculator of the risk of developing chronic kidney disease (CKD) in patients with diabetes mellitus (DM). To quote from: https://www.endocrincentr.ru/specialists/kalkulyatory/calc-hbp
5. Laptev D.N., Sorokin D.Yu. A medical decision-making assistance system based on artificial intelligence for primary adjustment of insulin pump parameters in children with type 1 diabetes mellitus. // Diabetes mellitus. — 2024. — Vol.27. — No.6. — pp.555-564. doi: https://doi.org/10.14341/DM13081
6. Kovaleva E.V., Eremkina A.K., Aynetdinova A.R., Milutina A.P., Mokrysheva N.G. The first Russian registry of hypoparathyroidism with a medical decision support system // Problems of endocrinology. — 2021. — Vol. 67. — No. 4. — pp. 8-12. doi: https://doi.org/10.14341/probl12796
7. Almuttairi H.M.A. Types of neural networks and their application / H.M.A. Almuttairi // Actual problems of society, economics and law in the context of global challenges: proceedings of the XXI International Scientific and Practical Conference, Moscow, June 14, 2023. – St. Petersburg: Printing Shop, 2023. – pp. 39-44.
8. Wang L, Zhang L, Zhu M, et al. Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. Med Image Anal. 2020; 61:101665. doi: https://doi.org/10.1016/j.media.2020.101665
9. Trukhin A.A., Zakharova S.M., Dunaev M.E., and others. The role of artificial intelligence in the differential ultrasound diagnosis of thyroid nodules // Clinical and experimental thyroidology. — 2022. — Vol. 18. — No. 2. — pp. 32-38. doi: https://doi.org/10.14341/ket12730
10. Lysukhin D.D., Yakimov B.P., Shirshin E.A. and others. Development of artificial intelligence algorithms for morphological diagnosis of thyroid tumors. // Endocrine surgery. — 2023. — Vol.17. — No.4. — p.54. doi: https://doi.org/10.14341/serg12877
11. Barat M, Gaillard M, Cottereau AS, et al. Artificial intelligence in adrenal imaging: A critical review of current applications. Diagn Interv Imaging. 2023;104(1):37-42. doi: https://doi.org/10.1016/j.diii.2022.09.003
12. Batch KE, Yue J, Darcovich A, et al. Developing a Cancer Digital Twin: Supervised Metastases Detection From Consecutive Structured Radiology Reports. Front Artif Intell. 2022;5. doi: https://doi.org/10.3389/frai.2022.826402
13. Abdullaev M.A., Kantemirova B.I., Bashkina O.A. and others. Prospects for the use of artificial intelligence in pharmacogenetic research: a literary review. // Acta Biomedica Scientifica. — 2024. — Vol.9. — No.5. — pp.12-21. doi: https://doi.org/10.29413/ABS.2024-9.5.2
14. Tarumi S, Takeuchi W, Chalkidis G, et al. Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus. Methods Inf Med. 2021;60(S 01):e32-e43. doi: https://doi.org/10.1055/s-0041-1728757
15. Chan PZ, Jin E, Jansson M., et al. AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review. J Med Internet Res. 2024;26:e58892. doi: https://doi.org/10.2196/58892
16. Alliance in the field of artificial intelligence, Analytical Center under the Government of the Russian Federation and the Ministry of Economic Development: Code of Ethics in the field of AI. To quote from: https://ethics.a-ai.ru/
Review
For citations:
Pershina-Miliutina A.P., Telegina M.A., Еrshova E.V., Komshilova K.A., Еrshova P.A. Artificial intelligence in endocrinology: Breakthrough technologies and prospects. Obesity and metabolism. 2025;22(2):118-122. (In Russ.) https://doi.org/10.14341/omet13238

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).