1. Das K, Cockerell CJ, Patil A, et al. Machine Learning and Its Application in Skin Cancer. Int J Environ Res Public Health 2021;18:13409. doi: 10.3390/ijerph182413409.
2. Walocko FM, Tejasvi T. Teledermatology Applications in Skin Cancer Diagnosis. Dermatol Clin 2017;35:559-63. doi: 10.1016/j.det.2017.06.002.
3. Chao E, Meenan CK, Ferris LK. Smartphone-Based Applications for Skin Monitoring and Melanoma Detection. Dermatol Clin 2017;35:551-7. doi: 10.1016/j.det.2017.06.014.
4. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ 2020;368:m645. doi: 10.1136/bmj.m645. Erratum for: BMJ 2020;368:m127. Erratum for: BMJ 2020;368:m428.
5. Abbasi J. Artificial Intelligence-Based Skin Cancer Phone Apps Unreliable. JAMA 2020;323:1336. doi: 10.1001/jama.2020.4543.
6. Speeckaert R, Hoorens I, Corthals S, et al. Comparison of methods to estimate the affected body surface area and the dosage of topical treatments in psoriasis and atopic dermatitis: the advantage of a picture-based tool. J Eur Acad Dermatol Venereol 2019;33:1726-32. doi: 10.1111/jdv.15726.
7. Speeckaert R, Lambert J, Delbaere L, Lesseliers T, van Geel N. The reliability of the Self-Assessment Cutaneous Inflammatory Disease Extent Score (SA-CIDES) and the rule of hands to assess the involved body surface area in psoriasis and eczema. Br J Dermatol 2021;184:171-3. doi: 10.1111/bjd.19430.
8. van Galen LS, Xu X, Koh MJA, Thng S, Car J. Eczema apps conformance with clinical guidelines: a systematic assessment of functions, tools and content. Br J Dermatol 2020;182:444-53. doi: 10.1111/bjd.18152.
9. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med 2020;26:900-8. doi: 10.1038/s41591-020-0842-3.
10. Biagioni RB, Carvalho BV, Manzioni R, Matielo MF, Brochado Neto FC, Sacilotto R. Smartphone application for wound area measurement in clinical practice. J Vasc Surg Cases Innov Tech 2021;7:258-261. doi: 10.1016/j.jvscit.2021.02.008.