APPLICATION AND USE OF AI (ARTIFICIAL INTELLIGENCE) IN MEDICINE

Authors

  • Feruza Shamansurovna Tukhtakhodjaeva Assistant of the Department of Biomedical Engineering, biophysics and informatics of the Tashkent Medical Academy
  • Mironshokh Nodirbek o‘g‘li Murodullayev Tashkent Medical Academy, student of Group 203, direction of Management
  • Iroda Ilhomovna Khayitova Associate Professor of “Information and Communication Technologies” Department of Bukhara Institute of Engineering and Technology

Keywords:

Artificial Intelligence (AI), medicine, healthcare diagnosis, treatment natural language processing, robotics, personalized medicine, Electronic Health Records (EHR).

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in the field of medicine, revolutionizing how healthcare is delivered, from diagnosis to treatment and beyond. This comprehensive article delves into the multifaceted applications and profound impact of AI in the medical domain. It traces the history and evolution of AI in medicine, exploring the various types of AI technologies employed, including machine learning, natural language processing, and robotics.

References

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216-1219.

ИИ Хайитова. Технология организации хранения данных в информационной системе. Молодой ученый 2016. (7) 24-26.

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.

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

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347-1358.

Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. The New England Journal of Medicine, 378(11), 981-983.

Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.

Sullivan, C. (2019). Artificial intelligence: Applications in clinical practice. Nursing Management, 50(10), 26-31.

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities, and challenges. Briefings in Bioinformatics, 19(6), 1236-1246.

Rudin, C., & Bates, J. (2019). Making machine learning models interpretable. Journal of Machine Learning Research, 20(1), 801-808.

Hassanpour, S., Langlotz, C. P., & Amrhein, T. J. (2016). Characterization of missed and pending critical findings in radiology reports. Journal of Digital Imaging, 29(4), 470-477.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.

World Health Organization. (2020). Ethical considerations in developing a public health response to pandemic influenza. World Health Organization.

Liyanage, H., Liaw, S. T., & Jonnagaddala, J. (2017). Artificial intelligence in primary health care: perceptions, issues, and challenges. Yearbook of Medical Informatics, 26(1), 41-46.

Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

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Published

2023-09-21

How to Cite

Tukhtakhodjaeva , F. S., Murodullayev , M. N. o‘g‘li, & Khayitova, I. I. (2023). APPLICATION AND USE OF AI (ARTIFICIAL INTELLIGENCE) IN MEDICINE. Educational Research in Universal Sciences, 2(9 SPECIAL), 302–309. Retrieved from http://erus.uz/index.php/er/article/view/3750

Issue

Vol. 2 No. 9 SPECIAL (2023): Educational Research in Universal Sciences (ERUS)

Section

Articles

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Creative Commons License

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