Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease. Shern Ping Choy et al. NPJ Digit Med 2023 9 (1) 180

https://phgkb.cdc.gov/PHGKB/phgHome.action?query=Machine%20learning&action=search&Mysubmit=Search From the abstract: "We searched for studies applying deep learning to skin images, excluding benign/malignant lesions. The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86–98; n?=?11), rosacea (94%, 90–97; n?=?4), eczema (93%, 90–99; n?=?9) and psoriasis (89%, 78–92; n?=?8) was high. "