J
Justin M. Ko
Researcher at Stanford University
Publications - 82
Citations - 10370
Justin M. Ko is an academic researcher from Stanford University. The author has contributed to research in topics: Medicine & Alopecia areata. The author has an hindex of 15, co-authored 62 publications receiving 7124 citations. Previous affiliations of Justin M. Ko include Harvard University & Eastern Virginia Medical School.
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
Journal ArticleDOI
Induction and exacerbation of psoriasis with TNF-blockade therapy: A review and analysis of 127 cases
TL;DR: Biopsies of TNF-blockade-induced lesions may reveal what cytokines and cell types drive the development of these lesions, and there is a need to develop an algorithm to treat this paradoxical side effect of therapy with T NF-blockers.
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Safety and efficacy of the JAK inhibitor tofacitinib citrate in patients with alopecia areata
Milène Kennedy Crispin,Justin M. Ko,Brittany G. Craiglow,Shufeng Li,Gautam Shankar,Jennifer Urban,J.C. Chen,Jane E. Cerise,Ali Jabbari,Mårten C.G. Winge,M. Peter Marinkovich,Angela M. Christiano,Anthony E. Oro,Brett A. King +13 more
TL;DR: At the dose and duration studied, tofacitinib is a safe and effective treatment for severe AA, though it does not result in a durable response.
Journal ArticleDOI
Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +6 more
TL;DR: This corrects the article to show that the method used to derive the H2O2 “spatially aggregating force” is based on a two-step process, not a single step, called a “shots fired” process.
Journal ArticleDOI
Prospective assessment of computer-aided detection in interpretation of screening mammography.
TL;DR: Routine use of CAD while interpreting screening mammograms significantly increases recall rates, has no significant effect on positive predictive value for biopsy, and can increase cancer detection rate by at least 4.7% and sensitivity by atAt least 4%.