Year: 2025
Type: article
Abstract: To address the diversity of skin conditions and the low prevalence of skin cancers, we curated a large hospital dataset (National Information Society Agency, Seoul, Korea [NIA] dataset; 70 diseases, 152,443 images) and collected real-world webapp data ( https://modelderm.com ; 1,691,032 requests). We propose a conservative evaluation method by assessing sensitivity in hospitals and specificity in real-world use, assuming all malignancy predictions were false positives. Based on three differential diagnoses, skin cancer sensitivity in Korea was 78.2% (NIA) and specificity was 88.0% (webapp). Top-1 and Top-3 accuracies for 70 diseases (NIA) were 43.3% and 66.6%, respectively. Analysis of webapp data provides insights into disease prevalence and public interest across 228 countries. Malignancy predictions were highest in North America (2.6%) and lowest in Africa (0.9%), while benign tumors were most common in Asia (55.5%), and infectious diseases were most prevalent in Africa (17.1%). These findings suggest that AI can aid global dermatologic surveillance.
Source: npj Digital Medicine
Institutions: University of Basel, Inje University, Inje University Sanggye Paik Hospital, Seoul National University, Seoul National University Hospital, Chonnam National University, Yonsei University, Yonsei University Health System, Asan Medical Center, University of Ulsan, Pusan National University, SNUH SMG-SNU Boramae Medical Center, Hallym University, Hallym University Kangnam Sacred Heart Hospital, Pontificia Universidad Católica de Chile
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,090
OpenAlex ID: W4414967967