Best Clinical AI Tools for Dermatology

Dermatology has more than 3,000 described conditions, making the specialty's diagnostic vocabulary among the largest in medicine. The visual nature of diagnosis has made dermatology a natural fit for AI-assisted image analysis (studies in Nature and JAMA Dermatology have shown deep learning models achieving dermatologist-level accuracy in melanoma detection). But traditional CDS tools serve a different role: they support the cognitive work that follows the visual diagnosis. Psoriasis management exemplifies the complexity. Moderate-to-severe plaque psoriasis now has 10+ FDA-approved biologic and targeted therapies spanning five distinct mechanisms: TNF-alpha inhibitors (adalimumab, etanercept, infliximab, certolizumab), IL-12/23 inhibitors (ustekinumab), IL-23 inhibitors (guselkumab, risankizumab, tildrakizumab), IL-17 inhibitors (secukinumab, ixekizumab, brodalumab, bimekizumab), and PDE4 inhibitors (apremilast). Selecting among these requires weighing efficacy data, safety profiles, patient comorbidities (TB screening, hepatitis B status, IBD history), insurance coverage, and patient preference. Atopic dermatitis has undergone a similar therapeutic expansion, with dupilumab, tralokinumab, abrocitinib, and upadacitinib joining the treatment landscape. Skin cancer management — from Mohs surgery indications to immunotherapy for advanced melanoma — adds another dimension. Our dermatology evaluation assesses how well CDS tools help dermatologists navigate these increasingly complex treatment algorithms.