Artificial intelligence models for the automated detection of skin tumors in children: systematic review of diagnostic accuracy and clinical validation
DOI:
https://doi.org/10.51798/sijis.v6i4.1109Keywords:
Artificial intelligence; Pediatric skin tumors; Diagnostic accuracy; Clinical validation; Deep learningAbstract
Introduction: Pediatric skin tumors are rare, accounting for less than 2% of childhood malignancies, and 20–30% of pediatric melanomas are initially misdiagnosed. Although adult-focused AI models for skin cancer often achieve AUC values above 0.90, their applicability in children is limited by scarce pediatric data and minimal external validation. This review synthesizes current evidence on the diagnostic accuracy and clinical validation of AI models for automated detection of pediatric skin tumors. Methods: We systematically searched PubMed, Embase, Scopus, Web of Science, and the Cochrane Library for studies published from January 2000 through March 2025. Eligible studies evaluated AI-based diagnosis or classification of skin tumors in pediatric populations and reported diagnostic accuracy metrics. Risk of bias was assessed. Results: Nine studies met inclusion criteria, but only two provided primary quantitative pediatric data. One study that incorporated 1,536 pediatric images into model training and tested performance in a pediatric set (n = 674) demonstrated a 7% mean AUC improvement (p = 0.003) and 162 fewer false positives (p = 0.0014). An earlier model trained predominantly on adult images achieved high sensitivity (98%) but low specificity (44%). Three systematic reviews reported diagnostic accuracies of 67–99% for general skin cancer detection, consistently emphasizing the severe shortage of pediatric cases in major image repositories. Additional narrative reviews and one protocol confirmed that fewer than 10% of AI dermatology studies focus on children. Conclusions: AI shows meaningful potential for improving pediatric skin tumor detection, and inclusion of pediatric data clearly enhances performance. However, progress is constrained by limited high-quality datasets and a lack of rigorous validation. Dedicated pediatric image repositories and prospective clinical trials are essential for advancing reliable clinical implementation.
References
AI-MEL: Image Analysis and Machine Learning for Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults (MELCAYA-AI-MEL). (2022). ClinicalTrials.gov Identifier: NCT05643661. Retrieved from https://www.clinicaltrials.gov/study/NCT06621810
Brănișteanu, D. E., Porumb-Andrese, E., Stărică, A., Munteanu, A. C., Toader M. P., Zemba, M., Porumb, V., Cozmin, M., Moraru, A. D., Nicolescu, A. C., & Brănișteanu, D. C. (2023). Differences and similarities in epidemiology and risk factors for cutaneous and uveal melanoma. Medicina, 59(5), 943. https://doi.org/10.3390/medicina59050943
Burshtein, J., & Buethe, M. G. (2024). Artificial Intelligence in Dermatology: A review of literature and Application to Pediatric Dermatology. SKIN the Journal of Cutaneous Medicine, 8(1), 1250–1257. https://doi.org/10.25251/skin.8.1.5
Cai, Z. R., Kim, J., Rezaei, S. J., Chen, M. L., Touma, F., Zhu, C., Onyeka, S., Fonjungo, F., Omiye, J. A., Krakowski, I., Nwandu, L., Biossonnette, R., Ko, J. M., & Linos, E. (2025). Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis. The British journal of dermatology, 193(5), 847–855. https://doi.org/10.1093/bjd/ljaf250
Christie-Nguyen, P., Tschen, J. A., & Saleh, H. M. (2025, April 4). Dermatopathology evaluation of tumors. StatPearls - NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK614157/
Dinnes, J., Deeks, J. J., Grainge, M. J., Chuchu, N., Di Ruffano, L. F., Matin, R. N., Thomson, D. R., Wong, K. Y., Aldridge, R. B., Abbott, R., Fawzy, M., Bayliss, S. E., Takwoingi, Y., Davenport, C., Godfrey, K., Walter, F. M., & Williams, H. C. (2018). Visual inspection for diagnosing cutaneous melanoma in adults. Cochrane Library, 2018(12). https://doi.org/10.1002/14651858.cd013194
Furriel, B. C. R. S., Oliveira, B. D., Prôa, R., Paiva, J. Q., Loureiro, R. M., Calixto, W. P., Reis, M. R. C., & Giavina-Bianchi, M. (2024). Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review. Frontiers in medicine, 10, 1305954. https://doi.org/10.3389/fmed.2023.1305954
Guy, G. P., Ekwueme, D. U., Tangka, F. K., & Richardson, L. C. (2012). Melanoma treatment costs. American Journal of Preventive Medicine, 43(5), 537–545. https://doi.org/10.1016/j.amepre.2012.07.031
Hamm, H., & Höger, P. H. (2011). Skin tumors in childhood. Deutsches Ärzteblatt International, 108(20), 347. Ärzteblatt International. https://doi.org/10.3238/arztebl.2011.0347
ISIC Challenge. (n.d.). https://challenge.isic-archive.com/
Li, Z., Koban, K. C., Schenck, T. L., Giunta, R. E., Li, Q., & Sun, Y. (2022). Artificial intelligence in Dermatology Image Analysis: Current developments and future trends. Journal of Clinical Medicine, 11(22), 6826. https://doi.org/10.3390/jcm11226826
Lilhore, U. K., Sharma, Y. K., Simaiya, S., Alroobaea, R., Baqasah, A. M., Alsafyani, M., & Alhazmi, A. (2025). SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-98205-7
Mehta, P. P., Sun, M., Betz-Stablein, B., Halpern, A., Soyer, H. P., Weber, J., Kose, K., & Rotemberg, V. (2023). Improving Artificial Intelligence−Based diagnosis on pediatric skin lesions. Journal of Investigative Dermatology, 143(8), 1423-1429.e1. https://doi.org/10.1016/j.jid.2022.08.058
Melarkode, N., Srinivasan, K., Qaisar, S. M., & Plawiak, P. (2023). AI-powered diagnosis of skin cancer: A contemporary review, open challenges and future research directions. Cancers, 15(4), 1234. https://doi.org/10.3390/cancers15041183
Monheit, G., Cognetta, A. B., Ferris, L., Rabinovitz, H., Gross, K., Martini, M., Grichnik, J. M., Mihm, M., Prieto, V. G., Googe, P., King, R., Toledano, A., Kabelev, N., Wojton, M., & Gutkowicz-Krusin, D. (2010). The performance of MelaFind. Archives of Dermatology, 147(2), 188. https://doi.org/10.1001/archdermatol.2010.302
Nebeska, K., Souckova, L., Stepanova, R., & Demlova, R. (2025). Exploring the publication gap in pediatric randomized clinical trials: completed vs. uncompleted pediatric clinical trials. Frontiers in Medicine, 12. https://doi.org/10.3389/fmed.2025.1590125
Saiyed, F. K., Hamilton, E. C., & Austin, M. T. (2017). Pediatric melanoma: incidence, treatment, and prognosis. Pediatric health, medicine and therapeutics, 8, 39–45. https://doi.org/10.2147/PHMT.S115534
Sinha, S., Lin, G., Zubkov, M., Wu, R., & Feng, H. (2021). Geographic distribution and characteristics of the pediatric dermatology workforce in the United States. Pediatric Dermatology, 38(6), 1523–1528. https://doi.org/10.1111/pde.14824
Wei, M. L., Tada, M., So, A., & Torres, R. (2024). Artificial intelligence and skin cancer. Frontiers in medicine, 11, 1331895. https://doi.org/10.3389/fmed.2024.1331895
Yu, J., Cheong, I. H., Kozlakidis, Z., & Wang, H. (2025). Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China. Frontiers in Digital Health, 7. https://doi.org/10.3389/fdgth.2025.1544520
Zama, D., Borghesi, A., Ranieri, A., Manieri, E., Pierantoni, L., Andreozzi, L., Dondi, A., Neri, I., Lanari, M., & Calegari, R. (2024). Perspectives and challenges of telemedicine and Artificial intelligence in Pediatric Dermatology. Children, 11(11), 1401. https://doi.org/10.3390/children11111401
Zbrzezny, A. M., & Krzywicki, T. (2025). Artificial Intelligence in Dermatology: A review of methods, clinical applications, and perspectives. Applied Sciences, 15(14), 7856. https://doi.org/10.3390/app15147856
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Elizabeth Arellano Pacheco, Jessica Marissa García Rodríguez, Fátima Esmeralda Ortega Ortega, Almeida Orellana Paola Carolina, Akemi Rocío Plaza Alcívar, Stephanie Saldaña Guerrero
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
