Artificial intelligence in dermatology: Challenges and perspectives Review


Authors: Liopyris, K.; Gregoriou, S.; Dias, J.; Stratigos, A. J.
Review Title: Artificial intelligence in dermatology: Challenges and perspectives
Abstract: Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians’ diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices. © 2022, The Author(s).
Keywords: melanoma; dermoscopy; skin cancer; artificial intelligence; diagnosis; prevention; machine learning; teledermatology
Journal Title: Dermatology and Therapy
Volume: 12
Issue: 12
ISSN: 2193-8210
Publisher: Adis Int Ltd  
Date Published: 2022-12-01
Start Page: 2637
End Page: 2651
Language: English
DOI: 10.1007/s13555-022-00833-8
PROVIDER: scopus
PMCID: PMC9674813
PUBMED: 36306100
DOI/URL:
Notes: Article -- Export Date: 1 December 2022 -- Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors