Artificial intelligence application in dermatology



如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅或者付费存取

详细

Dermatology is a field of medicine where morphological studies can be applied successfully for skin diseases diagnostics due to easy access and visualization of pathological site. Artificial intelligence was emerged as a powerful instrument in biomedicine where skin diseases can be considered to be one of the most representative subject for it’s application.

Present paper describes results of research data about artificial intelligence technologies adaptation in dermatology. Total 120 research papers were analyzed published between 2020-2025 in accordance to Pubmed database. The survey underlines usefulness of artificial intelligence technologies as a tool for skin cancer diagnostics. Representative number of images is crucial for melanoma diagnostics machine learning and neural network-based tools due to evident heterogeneity of a tumor. Images augmentation can increase efficiency of artificial intelligence based algorithms for atopic dermatitis, alopecia, rosacea, and acne diagnostics. Besides, artificial intelligence can be applied for educational purposes in dermatology professional trainings.

In a meantime patients express concerns about ethical aspects of artificial intelligence integration into clinical workflow that allows to consider this approach as innovative but additional tool for medical practice.

全文:

受限制的访问

作者简介

Tatiana Ruksha

Krasnoyarsk State Medical University

编辑信件的主要联系方式.
Email: tatyana_ruksha@mail.ru
ORCID iD: 0000-0001-8142-4283

professor, head of pathophysiology department

俄罗斯联邦, Krasnoyarsk, P. Zeleznyak str., 1

Ekaterina Lapkina

krasnoyarsk State Medical University

Email: e.z.lapkina@mail.ru
ORCID iD: 0000-0002-7226-9565

Ass. professor of Pathophysiology Dept.

俄罗斯联邦, Krasnoyarsk, P. Zeleznyaka str., 1

参考

  1. Stanton R. B. Artificial Intelligence // Nature. 1971. Vol. 234. P. 279–280. doi: 10.1038/234279b0
  2. Wang H., Fu T., Du Y. et al. Scientific discovery in the age of artificial intelligence // Nature. 2023 Aug. Vol. 620, N. 7972. P. 47–60. doi: 10.1038/s41586-023-06221-2
  3. Shanahan M., McDonell K., Reynolds L. Role play with large language models // Nature. 2023. Vol. 623, N. 7987. P. 493–498. doi: 10.1038/s41586-023-06647-8
  4. Feng J., Wang Q., Qiu H., Liu L. Retrieval In Decoder benefits generative models for explainable complex question answering // Neural Netw. 2025 Jan. Vol. 181. P. 106833. doi: 10.1016/j.neunet.2024.106833
  5. Ananthaswamy А. How close is AI to human-level intelligence? // Nature. 2024 Dec. Vol. 636, N. 8041. P. 22-25. doi: 10.1038/d41586-024-03905-1.
  6. Zhang M., Yang Q., Lou J., et al. A new strategy to HER2-specific antibody discovery through artificial intelligence-powered phage display screening based on the Trastuzumab framework // Biochim Biophys Acta Mol Basis Dis. 2025 Mar 7, Vol. 1871, N. 5. P. 167772. doi: 10.1016/j.bbadis.2025.167772
  7. King А. Four ways to power-up AI for drug discovery // Nature. 2025 Feb 27. doi: 10.1038/d41586-025-00602-5
  8. Nordmann T.M., Anderton H., Hasegawa A. et al. Spatial proteomics identifies JAKi as treatment for a lethal skin disease // Nature. 2024 Nov. Vol. 635, N. 8040. P. 1001–1009. doi: 10.1038/s41586-024-08061-0
  9. Modiri O., Ebriani J., Davis J. Can AI models assist patients in screening for non-melanoma skin cancer? Evaluating diagnostic accuracy of ChatGPT and Gemini using clinical images // Arch Dermatol Res. 2025 Mar 12. Vol. 317, N. 1. P. 555. doi: 10.1007/s00403-025-04062-9
  10. Mudhar H.S., Milman T., Stevenson S., et al. PRAME expression by immunohistochemistry and reverse transcription quantitative PCR in conjunctival melanocytic lesions-a comprehensive clinicopathologic study of 202 cases and correlation of cytogenetics with PRAME expression in challenging conjunctival melanocytic lesions // Hum Pathol. 2023 Apr. Vol. 134. P. 1-18. doi: 10.1016/j.humpath.2023.02.002
  11. Cui Y., Li Y., Miedema J.R., Edmiston S.N., Farag S.W., Marron J.S., Thomas N.E. Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images-Nevus and Melanoma. Cancers (Basel). 2024;16(15):2616. doi: 10.3390/cancers16152616
  12. Maher N.G., Danaei Mehr H., Cong C., Adegoke N.A., Vergara I.A., Liu S., Scolyer R.A. Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin-stained histopathology slides. J Eur Acad Dermatol Venereol. 2024;38(12):2250-2258. doi: 10.1111/jdv.20307
  13. Esimbekova A.R., Palkina N.V., Zinchenko I.S., et al. Focal adhesion alterations in G0-positive melanoma cells // Cancer Med. 2023 Mar. Vol. 12, N. 6. P. 7294-7308. doi: 10.1002/cam4.5510
  14. Komina A.V., Palkina N.V., Aksenenko M.B., et al. Semaphorin-5A downregulation is associated with enhanced migration and invasion of BRAF-positive melanoma cells under vemurafenib treatment in melanomas with heterogeneous BRAF status // Melanoma Res. 2019 Oct. Vol. 29, N. 5. P. 544-548. doi: 10.1097/CMR.0000000000000621
  15. Azimi A., Bi L., Bonfil A., Teh R., et al. Integrated analysis of proteomic and dermoscopy imaging data improves non-invasive classification of benign nevi and melanoma // J Invest Dermatol. 2025 Feb 13:S0022-202X(25)00102-2. doi: 10.1016/j.jid.2025.01.022
  16. Kuppanda P.M., Janda M., Soyer H.P., Caffery L.J. What Are Patients' Perceptions and Attitudes Regarding the Use of Artificial Intelligence in Skin Cancer Screening and Diagnosis? Narrative Review // J Invest Dermatol. 2025 Feb 27:S0022-202X(25)00080-6. doi: 10.1016/j.jid.2025.01.013
  17. Kimball A.B., Jemec G.B.E., Alavi A., et al. Secukinumab in moderate-to-severe hidradenitis suppurativa (SUNSHINE and SUNRISE): week 16 and week 52 results of two identical, multicentre, randomised, placebo-controlled, double-blind phase 3 trials // Lancet. 2023 Mar 4. Vol. 401, N. 10378. P. 747–761. doi: 10.1016/S0140-6736(23)00022-3
  18. Demirel Öğüt N., Ayanoğlu M.A., Koç Yıldırım S., et al. Are IL-17 inhibitors superior to IL-23 inhibitors in reducing systemic inflammation in moderate-to-severe plaque psoriasis? A retrospective cohort study // Arch Dermatol Res. 2025 Jan 13. Vol. 317, N. 1. P. 232. doi: 10.1007/s00403-024-03768-6
  19. Zhang Y., Qian H., Kuang Y.H., et al. Evaluation of the inflammatory parameters as potential biomarkers of systemic inflammation extent and the disease severity in psoriasis patients // Arch Dermatol Res. 2024 May 24. Vol. 316, N. 6. P. 229. doi: 10.1007/s00403-024-02972-8
  20. Hawro M., Sahin E., Steć M., et al. A comprehensive, tri-national, cross-sectional analysis of characteristics and impact of pruritus in psoriasis // J Eur Acad Dermatol Venereol. 2022 Nov. Vol. 36, N. 11. P. 2064-2075. doi: 10.1111/jdv.18330
  21. Aggarwal S.L.P. Data augmentation in dermatology image recognition using machine learning // Skin Res Technol. 2019 Nov. Vol. 25, N. 6. P. 815-820. doi: 10.1111/srt.12726
  22. Greenfield D.A., Feizpour A., Evans C.L. Quantifying Inflammatory Response and Drug-Aided Resolution in an Atopic Dermatitis Model with Deep Learning // J Invest Dermatol. 2023 Aug. Vol. 143, N. 8. P. 1430-1438. doi: 10.1016/j.jid.2023.01.026
  23. Eskandari A., Sharbatdar M. Efficient diagnosis of psoriasis and lichen planus cutaneous diseases using deep learning approach // Sci Rep. 2024 Apr 27. Vol. 14, N. 1. P. 9715. doi: 10.1038/s41598-024-60526-4
  24. Achararit P., Manaspon C., Jongwannasiri C., et al. Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks // Eur J Dent. 2023 Oct. Vol. 17, N. 4. P. 1275-1282. doi: 10.1055/s-0042-1760300
  25. Kaya G., Tak A.Y. Evaluation of SALT score severity in correlation with trichoscopic findings in alopecia areata: a study of 303 patients // Arch Dermatol Res. 2025 Mar 7/ Vol. 317, N. 1. P. 523. doi: 10.1007/s00403-025-04026-z
  26. Bhardwaj V., Rodgers N., Harth O., Harth Y. Artificial Intelligence-Based Personalization of Treatment Regimen for Hair Loss: A 6-Month Clinical Trial // J Drugs Dermatol. 2025 Mar 1. Vol. 24, N. 3. P. 233-238. doi: 10.36849/JDD.8611
  27. Dorado Cortez C., Fakih A., Bruet M., et al. Impact of dermoscopy training associated with artificial intelligence on general practitioner residents // J Eur Acad Dermatol Venereol. 2024 Dec. Vol. 38, N. 12. P. 2323-2325. doi: 10.1111/jdv.20328
  28. Khawaja Z., Adhoni M.Z.U., Byrnes K.G. Generative artificial intelligence powered chatbots in urology // Curr Opin Urol. 2025 Mar 19. doi: 10.1097/MOU.0000000000001280
  29. Anil Ananthaswamy How close is AI to human-level intelligence? Nature. 2024;636(8041):22-25. doi: 10.1038/d41586-024-03905-1.
  30. Villalobos P., Ho A., Sevilla J. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2211.04325 (2024).
  31. Muñoz J.M., Bernacer J., Noë A., Thompson E. Why AI will never be able to acquire human-level intelligence // Nature. 2025 Jan. Vol. 637, N. 8047. P. 794. doi: 10.1038/d41586-025-00170-8.

补充文件

附件文件
动作
1. JATS XML

版权所有 © Eco-Vector,



СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77 - 86501 от 11.12.2023 г
СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ЭЛ № ФС 77 - 80653 от 15.03.2021 г
.