Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.

Han, Seung Seog; Park, Ilwoo; Eun Chang, Sung; Lim, Woohyung; Kim, Myoung Shin; Park, Gyeong Hun; Chae, Je Byeong; Huh, Chang Hun; Na, Jung-Im

doi:10.1016/j.jid.2020.01.019

Cited by 248Cutaneous Melanoma Detection and ManagementAI in cancer detectionArtificial Intelligence in Healthcare and Education

Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.

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Han, Seung Seog, Park, Ilwoo, Eun Chang, Sung, Lim, Woohyung, Kim, Myoung Shin, Park, Gyeong Hun, Chae, Je Byeong, Huh, Chang Hun +1 more·2020·Journal of Investigative Dermatology

DOI 10.1016/j.jid.2020.01.019

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Abstract

Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,300 images; 10 disorders) and SNU datasets (2,201 images; 134 disorders). The algorithm could accurately predict malignancy, suggest primary treatment options, render multi-class classification among 134 disorders, and improve the performance of medical professionals. The area under the curves for malignancy detection were 0.928 ± 0.002 (Edinburgh) and 0.937 ± 0.004 (SNU). The area under the curves of primary treatment suggestion (SNU) were 0.828 ± 0.012, 0.885 ± 0.006, 0.885 ± 0.006, and 0.918 ± 0.006 for steroids, antibiotics, antivirals, and antifungals, respectively. For multi-class classification, the mean top-1 and top-5 accuracies were 56.7 ± 1.6% and 92.0 ± 1.1% (Edinburgh) and 44.8 ± 1.2% and 78.1 ± 0.3% (SNU), respectively. With the assistance of our algorithm, the sensitivity and specificity of 47 clinicians (21 dermatologists and 26 dermatology residents) for malignancy prediction (SNU; 240 images) were improved by 12.1% (P < 0.0001) and 1.1% (P < 0.0001), respectively. The malignancy prediction sensitivity of 23 non-medical professionals was significantly increased by 83.8% (P < 0.0001). The top-1 and top-3 accuracies of four doctors in the multi-class classification of 134 diseases (SNU; 2,201 images) were increased by 7.0% (P = 0.045) and 10.1% (P = 0.0020), respectively. The results suggest that our algorithm may serve as augmented intelligence that can empower medical professionals in diagnostic dermatology.

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The effect of AI-based support on dermoscopic images has been studied.
“the effect of AI-based support on dermoscopic images has been studied.15,24”
From Development and assessment of an artificial intelligence–based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices (2021) · cites this paper as [15]
Several algorithms incorporating artificial intelligence have been developed to help interpret clinical and dermoscopic images for a variety of skin conditions.
“Several algorithms incorporating artificial intelligence (AI) have been developed to help interpret both clinical11,12,13,14,15 and dermoscopic16,17,18,19,20,21,22,23 images for a variety of skin conditions”
From Development and assessment of an artificial intelligence–based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices (2021) · cites this paper as [15]

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Authors: Han, S. S., Park, I., Eun Chang, S., Lim, W., Kim, M. S., Park, G. H., Chae, J. B., Huh, C. H., & Na, J.-I.
Affiliations: Chonnam National University (KR)
LG (South Korea) (KR)
Inje University (KR)
Hallym University (KR)
Seoul National University Bundang Hospital (KR)
Corresponding author: Na, Jung-Im
ORCID: Han, Seung Seog
Park, Ilwoo
Lim, Woohyung
Kim, Myoung Shin
Park, Gyeong Hun
Chae, Je Byeong
Huh, Chang Hun
Na, Jung-Im
Venue: Journal of Investigative Dermatology
Publisher: Elsevier BV
Published: 2020-09
Volume: 140
Issue: 9
Pages: 1753-1761
ISSN: 0022-202X (ISSN-L)
DOI: 10.1016/j.jid.2020.01.019
OpenAlex: W3014403957
Cited by: 248
References: 35
Citations by year: 2026: 23 · 2025: 43 · 2024: 42 · 2023: 49 · 2022: 55 · 2021: 25 · 2020: 11
FWCI: 12.64
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
Locations: Journal of Investigative Dermatology (publishedVersion)
PubMed (publishedVersion)
Funders: National Research Foundation of Korea (2017R1C1B5018396)
Chonnam National University Hospital Biomedical Research Institute (CRI18019-1 and CRI18094-2)
Topics: Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: ology, skin cancer diagnosis, skin diseases, deep neural network, augmented intelligence, malignancy prediction, non-medical professionals, dermatology education, dermatology residents
MeSH terms: Dermatologists, Deep Learning, Adolescent, Adult, Aged, Anti-Bacterial Agents, Antifungal Agents, Antiviral Agents, Clinical Competence, Dermatology, Drug Therapy, Computer-Assisted, Feasibility Studies, Female, Glucocorticoids, Humans, Image Interpretation, Computer-Assisted, Internship and Residency, Male, Middle Aged, Photography, ROC Curve, Skin, Skin Diseases, Skin Neoplasms, Dermoscopy, Young Adult, Datasets as Topic

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Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.

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