Diagnostic accuracy of dermoscopy
Abstract
The accuracy of the dinical diagnosis of cutaneous melanoma with the unaided eye is only about 60%. Dermoscopy, a non-invasive, in vivo technique for the microscopic examination of pigmented skin lesions, has the potential to improve the diagnostic accuracy. Our objectives were to review previous publications, to compare the accuracy of melanoma diagnosis with and without dermoscopy, and to assess the influence of study characteristics on the diagnostic accuracy. We searched for publications between 1987 and 2000 and identified 27 studies eligible for meta-analysis. The diagnostic accuracy for melanoma was significantly higher with dermoscopy than without this technique (log odds ratio 4.0 [95% CI 3.0 to 5.1] versus 2.7 [1.9 to 3.4]; an improvement of 49%, p = 0.001). The diagnostic accuracy of dermoscopy significantly depended on the degree of experience of the examiners. Dermoscopy by untrained or less experienced examiners was no better than clinical inspection without dermoscopy. The diagnostic performance of dermoscopy improved when the diagnosis was made by a group of examiners in consensus and diminished as the prevalence of melanoma increased. A comparison of various diagnostic algorithms for dermoscopy showed no significant differences in their diagnostic performance. A thorough appraisal of the study characteristics showed that most of the studies were potentially influenced by verification bias. In conclusion, dermoscopy improves the diagnostic accuracy for melanoma in comparison with inspection by the unaided eye, but only for experienced examiners.
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Dermoscopy adds additional value in primary care when used by an expert or trained user.
“Dermoscopy adds additional value in primary care when used by an expert or trained user27–30.”
From Dermoscopy practice guidelines for use in telemedicine (2022) · cites this paper as [27]
Current AI models for dermatology remain limited to isolated tasks, such as diagnosing skin cancer from dermoscopic images.
“current AI models for dermatology remain limited to isolated tasks, such as diagnosing skin cancer from dermoscopic images1.”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [1]
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- Authors
- Kittler, H., Pehamberger, H., Wolff, K., & Binder, M.
- Affiliations
- University of Vienna (AT)
- Corresponding author
- Kittler, H
- ORCID
- Venue
- The Lancet Oncology
- Publisher
- Elsevier BV
- Published
- 2002-03
- Volume
- 3
- Issue
- 3
- Pages
- 159-165
- ISSN
- 1470-2045 (ISSN-L)
- OpenAlex
- W2164273268
- Cited by
- 1,349
- References
- 0
- Citations by year
- 2026: 66 · 2025: 66 · 2024: 93 · 2023: 76 · 2022: 86 · 2021: 93 · 2020: 76 · 2019: 67 · 2018: 59 · 2017: 61 · 2016: 58 · 2015: 64 · 2014: 49 · 2013: 46 · 2012: 58
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet Oncology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › melanin and skin pigmentation
- Health Sciences › Medicine › Dermatology › Cancer and Skin Lesions
- Keywords
- dermoscopy, cutaneous melanoma, diagnostic accuracy, pigmented skin lesions, examiner experience, verification bias, diagnostic algorithm, consensus diagnosis
- MeSH terms
- Dermatology, Diagnosis, Differential, Female, Humans, Image Enhancement, Luminescent Measurements, Male, Melanoma, Microscopy, Neoplasm Staging, Sensitivity and Specificity, Skin Neoplasms
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