What is AI? Applications of artificial intelligence to dermatology
Open accessAbstract
In the past, the skills required to make an accurate dermatological diagnosis have required exposure to thousands of patients over many years. However, in recent years, artificial intelligence (AI) has made enormous advances, particularly in the area of image classification. This has led computer scientists to apply these techniques to develop algorithms that are able to recognize skin lesions, particularly melanoma. Since 2017, there have been numerous studies assessing the accuracy of algorithms, with some reporting that the accuracy matches or surpasses that of a dermatologist. While the principles underlying these methods are relatively straightforward, it can be challenging for the practising dermatologist to make sense of a plethora of unfamiliar terms in this domain. Here we explain the concepts of AI, machine learning, neural networks and deep learning, and explore the principles of how these tasks are accomplished. We critically evaluate the studies that have assessed the efficacy of these methods and discuss limitations and potential ethical issues. The burden of skin cancer is growing within the Western world, with major implications for both population skin health and the provision of dermatology services. AI has the potential to assist in the diagnosis of skin lesions and may have particular value at the interface between primary and secondary care. The emerging technology represents an exciting opportunity for dermatologists, who are the individuals best informed to explore the utility of this powerful novel diagnostic tool, and facilitate its safe and ethical implementation within healthcare systems.
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Extensive research has shown that convolutional neural network accuracy is comparable to that of dermatologists.
“Extensive research has shown that the accuracy of CNNs is comparable to that of dermatologists [ 32 - 34 ].”
From Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists (2025) · cites this paper as [32]
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- Authors
- Du‐Harpur, X., Watt, F. M., Luscombe, N. M., & Lynch, M. D.
- Affiliations
- Centre for Stem Cells and Regenerative Medicine Faculty of Life Sciences and Medicine King's College London 28th Floor, Tower Wing, Guy's Hospital London SE1 9RT UK
- Guy's Hospital (GB)
- Okinawa Institute of Science and Technology Graduate University (JP)
- Venue
- British Journal of Dermatology
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-03-29
- Volume
- 183
- Issue
- 3
- Pages
- 423-430
- ISSN
- 0007-0963 (ISSN-L)
- 1365-2133
- PMC
- PMC7497072
- OpenAlex
- W3001446745
- Cited by
- 355
- References
- 31
- Citations by year
- 2026: 50 · 2025: 94 · 2024: 93 · 2023: 45 · 2022: 30 · 2021: 28 · 2020: 15
- FWCI
- 10.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- British Journal of Dermatology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by-nc-nd)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Okinawa Institute of Science and Technology Graduate University (Okinawa Institute of Science and Technology Graduate University) (submittedVersion, cc-by-nc-nd)
- Funders
- Biotechnology and Biological Sciences Research Council (BB/M007219/1)
- Wellcome Trust (103760/Z/14/Z, 206439/Z/17/Z, 211276/E/18/Z, FC010110)
- Cancer Research UK (FC010110)
- Medical Research Council (FC010110, MR/L016311/1, MR/PO18823/1)
- 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
- artificial intelligence, dermatology, skin lesion classification, melanoma diagnosis, machine learning, artificial neural networks, deep learning, image classification, diagnostic accuracy, ethical considerations, primary-secondary care interface
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Dermatology, Humans, Neural Networks, Computer
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