Application of Artificial Intelligence Technologies in Medicine

Authors

  • Raimjanova Nafosat Nozimovna Bostanliq District Vocational College No. 2, Special Subject Teacher, Uzbekistan
  • Sobirov Asadullo Vohidjon ugli Bostanliq District Vocational College No. 2, Special Subject Teacher, Uzbekistan

DOI:

https://doi.org/10.55640/eijps-06-06-12

Keywords:

Artificial intelligence, machine learning, medical diagnostics

Abstract

This article examines the application of artificial intelligence (AI) technologies in modern medicine, including their advantages, limitations, and future prospects. The study focuses on the role of machine learning, deep learning, and natural language processing methods in diagnostics, treatment planning, genomics, and medical imaging analysis. The analysis conducted demonstrates that AI can reduce medical errors by up to 40% and enable early disease detection. At the same time, data security, algorithmic bias, and ethical issues remain significant challenges.

Downloads

Download data is not yet available.

References

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

Deloitte Center for Health Solutions. (2022). Global health care outlook: Are we finally seeing the shift to value? Deloitte Insights.

World Health Organization. (2023). Health workforce: Key facts. WHO. https://www.who.int/news-room/fact-sheets/detail/health-workforce

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., & Lungren, M. P. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv:1711.05225.

Johnson, A. E. W., Pollard, T. J., Shen, L., et al. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.

Rashidov, A. S., Yusupov, B. T., & Karimov, F. R. (2023). Implementing AI-based diagnostic systems in Uzbekistan healthcare: Initial results and challenges. Central Asian Journal of Medical Sciences, 9(2), 112-124.

Shen, D., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221-248.

De Fauw, J., Ledsam, J. R., Romera-Paredes, B., et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342-1350.

Ardila, D., Kiraly, A. P., Bharadwaj, S., et al. (2019). End-to-end lung cancer screening with deep learning on low-dose CT. Nature Medicine, 25(6), 954-961.

Somashekhar, S. P., Sepulveda, M.-J., Puglielli, S., et al. (2018). Watson for Oncology and breast cancer treatment recommendations. Annals of Oncology, 29(2), 418-423.

Rajpurkar, P., & Lungren, M. P. (2020). The current and future state of AI interpretation of medical images. New England Journal of Medicine, 382, 1690-1695.

Downloads

Published

2026-06-11

How to Cite

Raimjanova Nafosat Nozimovna, & Sobirov Asadullo Vohidjon ugli. (2026). Application of Artificial Intelligence Technologies in Medicine. European International Journal of Philological Sciences, 6(06), 47–49. https://doi.org/10.55640/eijps-06-06-12