The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.
Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.
Open accessAbstract
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.15,24”
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“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”
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Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146
Human-computer collaboration for skin cancer recognition
Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0942-0 · Cited by 1,024
Disparities in dermatology AI performance on a diverse, curated clinical image set
Daneshjou, R., Vodrahalli, K., Novoa, R. A., Jenkins, M., Liang, W., Rotemberg, V., Ko, J., Swetter, S. M., Bailey, E. E., Gevaert, O., et al. (2022) · Science Advances · doi:10.1126/sciadv.abq6147 · Cited by 544
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Metadata
- 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
- Venue
- Journal of Investigative Dermatology
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 140
- Issue
- 9
- Pages
- 1753-1761
- ISSN
- 0022-202X (ISSN-L)
- 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
- 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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