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https://doi.org/10.62836/jissh.v3i3.0022
Zhang, H., Chen, Y., Zhang, F., & Zhang, L. (2026). Artificial Intelligence in Skin Care Medical Education: Current Status, Challenges and Future Perspectives. Journal of Integrated Social Sciences and Humanities, 3(3), 0022. https://doi.org/10.62836/jissh.v3i3.0022
Volume 3, Issue 3, 2026

Artificial Intelligence in Skin Care Medical Education: Current Status, Challenges and Future Perspectives

Skincare education serves two connected audiences: patients and the general public, and medical students and aesthetic practitioners. Conventional teaching in both areas remains constrained—resources are unevenly distributed, consultation time is short, and content is largely one-size-fits-all—so it struggles to meet the growing demand for personalized, accessible skin-health guidance and standardized professional training. Recent progress in computer vision, large language models, multimodal generative AI, and immersive virtual reality has moved AI to the center of this field. For the public, AI tools provide visual assessment of skin conditions, round-the-clock personalized advice, plain-language explanations of ingredients, automated educational content, and continuous follow-up, making evidence-based knowledge far more reachable. For trainees, these technologies enable image-based diagnostic practice, learning paths for individual progress, risk-free virtual simulation, and automatically generated teaching materials, which relieves the shortage of quality clinical resources. However, AI still faces five obstacles in skin care practice: algorithmic bias that makes performance uneven across different skin types; hallucinated or fabricated output from generative models; overreliance on images without clinical information; biometric privacy risks; and content skewed by commercial interests. Future efforts should focus on four priorities—building datasets that represent all skin types and demographic groups, embedding clinician supervision into content workflows, fusing multi-source real-world data into scenario-based learning, and strengthening regulation. Used as a supervised adjunct rather than a replacement, AI can make skincare education more accessible, fairer, and more efficient through a sustainable human-machine partnership.

artificial intelligence skin care education patient health education dermatology medical education large language models virtual simulation teaching

References

  1. Li Y, Hu T, Xia X, et al. Knowledge, Attitude, and Practice Toward Photoaging in the Chinese Population: a Cross-Sectional Study. Scientific Reports 2024; 14(1): 5196. https://doi.org/10.1038/s41598-024-55691-5.
  2. Li D, Chen Q, Liu Y, et al. The Prevalence of Acne in Mainland China: a Systematic Review and Meta-Analysis. BMJ Open 2017; 7(4): e015354. https://doi.org/10.1136/bmjopen-2016-015354.
  3. Hansra NK, O’Sullivan P, Chen CL, et al. Medical School Dermatology Curriculum: Are We Adequately Preparing Primary Care Physicians? Journal of the American Academy of Dermatology 2009; 61(1): 23–29.e1. https://doi.org/10.1016/j.jaad.2008.11.912.
  4. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature 2017; 542: 115–118. https://doi.org/10.1038/nature21056.
  5. Chan KS, Zary N. Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Medical Education 2019; 5(1): e13930. https://doi.org/10.2196/13930.
  6. Chen R, Zhang Y, Choi S, et al. The Chatbots Are Coming: Risks and Benefits of Consumer-Facing Artificial Intelligence in Clinical Dermatology. Journal of the American Academy of Dermatology 2023; 89(4): 872–874. https://doi.org/10.1016/j.jaad.2023.05.088.
  7. Yang R, Tan TF, Lu W, et al. Large Language Models in Health Care: Development, Applications, and Challenges. Health Care Science 2023; 2(4):255–263. https://doi.org/10.1002/hcs2.61.
  8. Lewandowski M, Kropidłowska J, Kvinen A, et al. A Systemic Review of Large Language Models and Their Implications in Dermatology. Australasian Journal of Dermatology 2025; 66(4): e202–e208. https://doi.org/10.1111/ajd.14484.
  9. Lim ZV, Akram F, Ngo CP, et al. Automated Grading of Acne Vulgaris by Deep Learning with Convolutional Neural Networks. Skin Research and Technology 2020; 26(2):187–192. https://doi.org/10.1111/srt.12794.
  10. Han SS, Kim MS, Lim W, et al. Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. Journal of Investigative Dermatology 2018; 138(7):1529–1538. https://doi.org/10.1016/j.jid.2018.01.028.
  11. Brinker TJ, Faria BL, De Faria OM, et al. Effect of a Face-Aging Mobile App–Based Intervention on Skin Cancer Protection Behavior in Secondary Schools in Brazil: A Cluster-Randomized Clinical Trial. JAMA Dermatology 2020; 156(7): 737. https://doi.org/10.1001/jamadermatol.2020.0511.
  12. Buller DB, Berwick M, Lantz K, et al. Smartphone Mobile Application Delivering Personalized, Real-Time Sun Protection Advice: A Randomized Clinical Trial. JAMA Dermatology 2015; 151(5): 497–504. https://doi.org/10.1001/jamadermatol.2014.3889.
  13. Zhang Y, Chen R, Nguyen D, et al. Assessing the Ability of an Artificial Intelligence Chatbot to Translate Dermatopathology Reports into Patient-Friendly Language: A Cross-Sectional Study. Journal of the American Academy of Dermatology 2024; 90(2): 397–399. https://doi.org/10.1016/j.jaad.2023.09.072.
  14. Kamminga NCW, Kievits JEC, Plaisier PW, et al. Do Large Language Model Chatbots Perform Better Than Established Patient Information Resources in Answering Patient Questions? A Comparative Study on Melanoma. British Journal of Dermatology 2025; 192(2):306–315. https://doi.org/10.1093/bjd/ljae377.
  15. Mantena S, Johnson A, Oppezzo M, et al. Fine-Tuning LLMs in Behavioral Psychology for Scalable Health Coaching. npj Cardiovascular Health 2025; 2(1): 48. https://doi.org/10.1038/s44325-025-00083-5.
  16. Bennike NH, Oturai NB, Müller S, et al. Fragrance Contact Allergens in 5588 Cosmetic Products Identified Through a Novel Smartphone Application. Journal of the European Academy of Dermatology and Venereology 2018; 32(1):79–85. https://doi.org/10.1111/jdv.14513.
  17. Ananthapadmanabhan KP, Mukherjee S, Chandar P. Stratum Corneum Fatty Acids: Their Critical Role in Preserving Barrier Integrity During Cleansing. International Journal of Cosmetic Science 2013; 35(4): 337–345. https://doi.org/10.1111/ics.12042.
  18. Campbell J, Williams K, Woolery-Lloyd H. DermTok: How TikTok Is Changing the Landscape of Dermatology Patient Education. Journal of Drugs in Dermatology 2023; 22(3): 302–304. https://doi.org/10.36849/JDD.6676.
  19. Svendsen MT, Feldmann S, Tiedemann SN, et al. Improving Psoriasis Patients’ Adherence to Topical Drugs: a Systematic Review. Journal of Dermatological Treatment 2020; 31(8): 776–785. https://doi.org/10.1080/09546634.2019.1623371.
  20. Goktas P, Gulseren D, Tobin AM. Large Language and Vision Assistant in Dermatology: a Game Changer or Just Hype? Clinical and Experimental Dermatology 2024; 49(8): 783–792. https://doi.org/10.1093/ced/llae119.
  21. Donoso F, Peirano D, Longo C, et al. Gamified Learning in Dermatology and Dermoscopy Education: a Paradigm Shift. Clinical and Experimental Dermatology 2023; 48(9): 962–967. https://doi.org/10.1093/ced/llad177.
  22. Mangion SE, Phan TA, Zagarella S, et al. Medical School Dermatology Education: a Scoping Review. Clinical and Experimental Dermatology 2023; 48(6): 648–659. https://doi.org/10.1093/ced/llad052.
  23. Mao RQ, Lan L, Kay J, et al. Immersive Virtual Reality for Surgical Training: A Systematic Review. Journal of Surgical Research 2021; 268: 40–58. https://doi.org/10.1016/j.jss.2021.06.045.
  24. Gladstone HB, Raugi GJ, Berg D, et al. Virtual Reality for Dermatologic Surgery: Virtually a Reality in the 21st Century. Journal of the American Academy of Dermatology 2000; 42(1): 106–112. https://doi.org/10.1016/S0190-9622(00)90017-3.
  25. Nasir M, Tahir MZ, Zhou SK. Reducing Skin Tone Bias in Dermatology AI Via Sketch-Guided Multimodal Fusion. Scientific Reports 2026. https://doi.org/10.1038/s41598-026-59316-x.
  26. Omiye JA, Gui H, Rezaei SJ, et al. Large Language Models in Medicine: The Potentials and Pitfalls: A Narrative Review. Annals of Internal Medicine 2024; 177(2): 210–220. https://doi.org/10.7326/M23-2772.
  27. Kounidas G, Cleer I, Harriss E, et al. Usability Evaluation and Reporting for Mobile Health Apps Targeting Patients with Skin Diseases: a Systematic Review. Clinical and Experimental Dermatology 2025; 50(2): 387–394. https://doi.org/10.1093/ced/llae378.
  28. Sangers TE, Kittler H, Blum A, et al. Position Statement of the EADV Artificial Intelligence (AI) Task Force on AI-Assisted Smartphone Apps and Web-Based Services for Skin Disease. Journal of the European Academy of Dermatology and Venereology 2024; 38(1): 22–30. https://doi.org/10.1111/jdv.19521.
  29. Huang X, Chalmers AN. Review of Wearable and Portable Sensors for Monitoring Personal Solar UV Exposure. Annals of Biomedical Engineering 2021; 49(3): 964–978. https://doi.org/10.1007/s10439-020-02710-x.

Supporting Agencies

  1. Funding: National Natural Science Foundation of China (NSFC 82304052), Health Commission of Sichuan Province Medical Science and Technology Program (24LCYJPT05) and "Qimingxing" Research Fund for Young Talents.