Artificial intelligence for melanoma diagnosis: how can we deliver on the promise?
Open accessAbstract
Currently, diagnostic accuracy for melanoma is dependent on the experience and training of the treating doctor. In this issue of the Annals of Oncology, Haenssle et al. [1] have shown that a computer algorithm using convolutional neural networks outperformed the majority of 58 dermatologists tested in accurately diagnosing melanoma, with a median area under the receiver operating characteristic curve of 0.86 compared with 0.79, P<0.01. This, together with the recent landmark work by Esteva et al.
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Applications of DL techniques based on transfer learning have reported performance comparable to or better than that of human experts.
“The applications of DL techniques based on transfer learning have reported performance comparable to that of the human experts 5 , , 6 or better 7 ., 8”
From The need for uncertainty quantification in machine-assisted medical decision making (2019) · cites this paper as [7]
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“Developments in the field of computer vision have shown great promise in transferring to medical image analysis, and several techniques have been shown to perform as accurately as human observers (Haenssle et al., 2018; Mar and Soyer, 2018).”
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- Authors
- Mar, V. J., & Soyer, H. P.
- Affiliations
- The Alfred Hospital (AU)
- The University of Queensland (AU)
- Corresponding author
- Soyer, H.P.
- ORCID
- Venue
- Annals of Oncology
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 29
- Issue
- 8
- Pages
- 1625-1628
- ISSN
- 0923-7534 (ISSN-L)
- OpenAlex
- W2886533602
- Semantic Scholar
- a27255f3c792c289f7444eafeb7153641caa8244
- Cited by
- 37
- References
- 21
- Citations by year
- 2025: 5 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 5 · 2020: 6 · 2019: 6 · 2018: 2 · 2014: 1
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Annals of Oncology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- 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
- melanoma diagnosis, artificial intelligence, convolutional neural networks, computer-aided diagnosis, diagnostic accuracy, dermatologist comparison, skin cancer imaging
- MeSH terms
- Dermatologists, Deep Learning, Artificial Intelligence, Humans, Male, Melanoma
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