POSSIBILITIES OF USING ARTIFICIAL INTELLIGENCE IN DERMATOLOGY: REVOLUTIONIZING DIAGNOSIS AND TREATMENT

Authors

  • Ergashov Adkhamjon Tajimurodovich Associate professor of Medicinal and biological chemistry department of Tashkent Medical Academy
  • Tajimurodov Khamdamjon Adkhamjon o‘gli Assistant of Dermatovenereology and Cosmetology department of Tashkent Medical Academy

Keywords:

Artificial intelligence, dermoscopy, diagnosis

Abstract

This article explores the current applications of AI in dermatology, highlights its potential to address global dermatological challenges, and discusses its limitations and ethical considerations. By leveraging AI, dermatologists can achieve faster, more diagnoses that are accurate and improve access to dermatological care, especially in underserved regions.

References

1. Esteva, A., et al. "Dermatologist-level classification of skin cancer with deep neural networks." Nature, 2017; 542: 115–118.

2. Han, S. S., et al. "Keratinocytic skin cancer detection on the face using region-based convolutional neural network." JAMA Dermatology, 2020; 156(1): 29–37.

3. Vermesan O, Eisenhauer M, Sundmaeker H, et al. Internet of things cognitive transformation technology research trends and applications.

4. Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? J Arthroplast. 2018; 33(8):2358–2361. Doi: 10.1016/j.arth.2018.02.067. [DOI] [PubMed] [Google Scholar]

5. Kakhorova, M. A. (2024). THE USE OF QUALITATIVE AND MIXED METHODS INVESTIGATING LEARNERS IN THEIR CLASSROOMS. Academic research in educational sciences, (1), 579-587.

6. Kaxorova, M. A. (2024). THE PHENOMENA OF WORD FORMATION IN LATIN AS AN EXAMPLE OF CARDIOLOGICAL TERMS. Academic research in educational sciences, (1), 483-488.

7. Askaraliyevna, K. M. (2024). Essential Guidelines for Proficient Foreign Language Learning. Miasto Przyszłości, 52, 532-534.

8. Kakhorova, M. A. (2023). NUTRITION OF SURGICAL PATIENTS. Modern Scientific Research International Scientific Journal, 1(8), 172-180.

9. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37(2):505–515. doi: 10.1148/rg.2017160130. [DOI] [PMC free article] [PubMed] [Google Scholar]

10. Hogarty DT, Mackey DA, Hewitt AW. Current state and future prospects of artificial intelligence in ophthalmology: a review. Clin Exp Ophthalmol. 2019;47(1):128–139. doi: 10.1111/ceo.13381. [DOI] [PubMed] [Google Scholar]

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Published

2024-11-19

How to Cite

Tajimurodovich, E. A., & Adkhamjon o‘gli, T. K. (2024). POSSIBILITIES OF USING ARTIFICIAL INTELLIGENCE IN DERMATOLOGY: REVOLUTIONIZING DIAGNOSIS AND TREATMENT. EUROPEAN JOURNAL OF MODERN MEDICINE AND PRACTICE, 4(11), 347–350. Retrieved from http://inovatus.es/index.php/ejmmp/article/view/4490

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