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<p>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 (<a href="https://modelderm.com">https://modelderm.com</a>; 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月8日

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

摘要

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.