Artificial intelligence in dermatology: a comparative analysis of computer vision programs based on machine learning models

Objective: Comparative analysis of modern computer programs (smartphone programs – mobile applications) using artificial intelligence (AI) for diagnosis and dynamic monitoring of skin pathologies.

Material and methods. A total of 1,319 publications were identified for AI-powered computer programs using targeted searches in PubMed/MEDLINE and Google Scholar databases, as well as in the eLibrary and CyberLeninka electronic libraries for the period 2016–2025. Using queries focused on AI, convolutional neural networks (CNNs), computer programs (mobile apps), and dermatovenereology, a total of 1,319 publications were identified. After a multi-stage screening based on inclusion/exclusion criteria (including the availability of quantitative performance metrics), 9 key articles with specific descriptions of the computer programs (mobile apps) were selected. A search and subsequent analysis identified 9 computer programs (mobile apps): Google DermAssist, SkinIO, Melanoma Check, Derma Onko Check, SkinVision, Tibot, SkinScan, Aysa, and Skinive, which use AI to diagnose and monitor skin conditions.

Results. Effectiveness of the programs varies: Google DermAssist and Derma Onko Check demonstrated high accuracy (96–97%) and sensitivity (97–98%), while Skinive showed improvement in metrics over time from 2020 to 2021 (maximum sensitivity of 97.9% and specificity of 97.1%). Limitations include dependence on photo image quality, low effectiveness for rare conditions and dark skin tones, and the need for a biopsy to confirm a diagnosis. Mobile apps using CNN demonstrate high sensitivity (87–97.9%), but specificity varies significantly (70–98%), which may increase the number of additional consultations with specialist doctors when using these programs in diagnostics.

Conclusion. AI-based software (mobile apps) offer significant potential for increasing the accessibility and accuracy of skin pathology diagnostics, especially in remote areas and regions with a shortage of dermatovenereologists. Potential developments include integrating software with telemedicine, improving algorithms for diagnosing rare pathologies, and standardizing testing to improve the reproducibility of results.

1. Liopyris K., Gregoriou S., Dias J., Stratigos A.J. Artificial intelligence in dermatology: challenges and perspectives. Dermatol Ther. 2022; 12 (12): 2637–51. https://doi.org/10.1007/s13555-022-00833-8.

2. De A., Sarda A., Gupta S., Das S. Use of artificial intelligence in dermatology. Indian J Dermatol. 2020; 65 (5): 352–7. https://doi.org/10.4103/ijd.IJD_418_20.

3. Renders J.M., Simonart T. Role of artificial neural networks in dermatology. Dermatology. 2009; 219 (2): 102–4. https://doi.org/10.1159/000225933.

4. Sarvamangala D.R., Kulkarni R.V. Convolutional neural networks in medical image understanding: a survey. Evol Intell. 2022; 15 (1): 1–22. https://doi.org/10.1007/s12065-020-00540-3.

5. Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A. Artificial intelligence: basic terms and concepts, the application in healthcare and clinical medicine. FARMAKOEKONOMIKA. Sovremennaya farmakoekonomika i farmakoepidemiologiya / FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2024; 17 (3): 409–15 (in Russ.). https://doi.org/10.17749/2070-4909/farmakoekonomika.2024.267.

6. Daneshjou R., Smith M.P., Sun M.D., et al. Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping review. JAMA Dermatol. 2021; 157 (11): 1362–9. https://doi.org/10.1001/jamadermatol.2021.3129.

7. Haddadin S., Ganti L. The use of artificial intelligence to detect malignant skin lesions. Mayo Clin Proc Digit Health. 2024; 2 (2): 241–5. https://doi.org/10.1016/j.mcpdig.2024.04.003.

8. Guido N., Hagstrom E., Ibler E., et al. A novel total body digital photography smartphone application designed to detect and monitor skin lesions: a pilot study. J Surg Dermatol. 2021; 6 (2): 14. https://doi.org/.18282/jsd.v6.i2.177.

9. Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A., et al. The accuracy of the preliminary diagnosis of malignant melanocytic skin tumors using the artificial intelligence program ''Melanoma Check". Medical Bulletin of the Main Military Clinical Hospital named after N.N. Burdenko. 2025; 1: 42–51 (in Russ.). https://doi.org/10.53652/2782-1730-2025-6-1-42-51.

10. Lamotkin A.I., Korabelnikov D.I., Olisova O.Yu., Lamotkin I.A. Effectiveness of preliminary differential diagnosis of benign and malignant skin neoplasms using the Derma Onko Check artificial intelligence program. FARMAKOEKONOMIKA. Sovremennaya farmakoekonomika i farmakoepidemiologiya / FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2025; 18 (2): 261–70 (in Russ.). https://doi.org/10.17749/2070-4909/farmakoekonomika.2025.294.

11. Smak Gregoor A.M., Sangers T.E., Bakker L.J., et al. An artificial intelligence based app for skin cancer detection evaluated in a population based setting. NPJ Digit Med. 2023; 6 (1): 90. https://doi.org/10.1038/s41746-023-00831-w.

12. Marri S.S., Inamadar A.C., Janagond A.B., Albadri W. Analyzing the predictability of an artificial intelligence app (Tibot) in the diagnosis of dermatological conditions: a cross-sectional study. JMIR Dermatol. 2023; 6: e45529. https://doi.org/10.2196/45529.

13. Wadhawan T., Situ N., Lancaster K., et al. SkinScan©: a portable library for melanoma detection on handheld devices. Proc IEEE Int Symp Biomed Imaging. 2011; 2011: 133–6. https://doi.org/10.1109/ISBI.2011.5872372.

14. Marri S.S., Albadri W., Hyder M.S., et al. Efficacy of an artificial intelligence app (Aysa) in dermatological diagnosis: cross-sectional analysis. JMIR Dermatol. 2024; 7: e48811. https://doi.org/10.2196/48811.

15. Sokolov K., Shpudeiko V. Dynamics of the neural network accuracy in the context of modernization of the algorithms of skin pathology recognition. Indian J Dermatol. 2022; 67 (3): 312. https://doi.org/10.4103/ijd.ijd_1070_21.

16. Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A. Preliminary differential diagnosis of benign and malignant tumors from epidermal skin tissue using an artificial intelligence program “Derma Onko Check”. Current Problems of Health Care and Medical Statistics. 2025; 2: 223–42 (in Russ.). https://doi.org/10.24412/2312-2935-2025-2-223-242.

17. Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A., et al. The accuracy of the preliminary diagnosis of benign and malignant melanocytic skin tumors using the “Derma Onko Check” AI-based smartphone application. Medical Bulletin of the Main Military Clinical Hospital named after N.N. Burdenko. 2025; 2: 39–48 (in Russ.). https://doi.org/10.53652/2782-1730-2025-6-2-39-48.

18. Williamson S.M., Prybutok V. Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Appl Sci. 2024; 14 (2): 675. https://doi.org/10.3390/app14020675.