Planet-wide performance of a skin disease AI algorithm validated in Korea

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.

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Publication Details

Journal
npj Digital Medicine
Published
2025-10-08
DOI
https://doi.org/10.1038/s41746-025-01980-w
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Planet-wide performance of a skin disease AI algorithm validated in Korea

Sung Eun Chang, Hyun‐Sun Yoon, Myoung Shin Kim, Gábor Fábián et al.
npj Digital Medicine
Cutaneous Melanoma Detection and Management
article

Planet-wide performance of a skin disease AI algorithm validated in Korea

Sung Eun Chang, Hyun‐Sun Yoon, Myoung Shin Kim, Gábor Fábián, Kyung-Nam Bae, Alexander A. Navarini, Ju Hee Lee, Soo Ick Cho, Cristián Navarrete‐Dechent, Chong Hyun Won, Jee-Bum Lee, Seung Seog Han, Jihee Kim, Dong Hun Lee, Seong Hwan Kim, Jung Im Na
article en

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.

npj Digital MedicineVol. 8(1)
Chonnam National University (KR), Seoul National University (KR), Pontificia Universidad Católica de Chile (CL), University of Basel (CH), Yonsei University (KR), Seoul National University Hospital (KR), Seoul National University Bundang Hospital (KR), University of Ulsan (KR), Kangnam Sacred Heart Hospital (KR), Hallym University Kangnam Sacred Heart Hospital (KR), SNUH SMG-SNU Boramae Medical Center (KR), Inje University Sanggye Paik Hospital (KR), Dr Youth Clinic (KR), National University College (PR), University Dermatology (US), Institute for National Security Strategy (KR), Pusan National University (KR)
Openalex Percentile: Top 61%
Cutaneous Melanoma Detection and Management
DOIOpen Access PDFOpenAlex
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