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Evaluation of the diagnostic accuracy of an online artificial intelligence application for skin disease diagnosis

Zaar, Oscar ; Larson, Alexander ; Polesie, Sam ; Saleh, Karim LU ; Tarstedt, Mikael ; Olives, Antonio ; Suarez, Andrea ; Gillstedt, Martin and Neittaanmäki, Noora (2020) In Acta Dermato-Venereologica 100(16). p.1-6
Abstract

Artificial intelligence (AI) algorithms for automated classification of skin diseases are available to the con-sumer market. Studies of their diagnostic accuracy are rare. We assessed the diagnostic accuracy of an open-access AI application (Skin Image Search™) for recognition of skin diseases. Clinical images including tumours, infective and inflammatory skin diseases were collected at the Department of Dermatology at the Sahlgrenska University Hospital and uploaded for classification by the online application. The AI algorithm classified the images giving 5 differential diagno-ses, which were then compared to the diagnoses made clinically by the dermatologists and/or histologically. We included 521 images portraying 26 diagnoses. The... (More)

Artificial intelligence (AI) algorithms for automated classification of skin diseases are available to the con-sumer market. Studies of their diagnostic accuracy are rare. We assessed the diagnostic accuracy of an open-access AI application (Skin Image Search™) for recognition of skin diseases. Clinical images including tumours, infective and inflammatory skin diseases were collected at the Department of Dermatology at the Sahlgrenska University Hospital and uploaded for classification by the online application. The AI algorithm classified the images giving 5 differential diagno-ses, which were then compared to the diagnoses made clinically by the dermatologists and/or histologically. We included 521 images portraying 26 diagnoses. The diagnostic accuracy was 56.4% for the top 5 suggested diagnoses and 22.8% when only considering the most probable diagnosis. The level of diagnostic accuracy varied considerably for diagnostic groups. The online application demonstrated low diagnostic accuracy compared to a dermatologist evaluation and needs further development.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial intelligence, Dermato-logy, Online diagnostics, Skin disease
in
Acta Dermato-Venereologica
volume
100
issue
16
article number
adv00260
pages
6 pages
publisher
Medical Journals Limited
external identifiers
  • scopus:85091107771
  • pmid:32852557
ISSN
0001-5555
DOI
10.2340/00015555-3624
language
English
LU publication?
yes
id
63207a64-f3b4-4df3-ae28-2ac4ad5be32f
date added to LUP
2021-01-08 14:08:13
date last changed
2024-06-14 07:20:11
@article{63207a64-f3b4-4df3-ae28-2ac4ad5be32f,
  abstract     = {{<p>Artificial intelligence (AI) algorithms for automated classification of skin diseases are available to the con-sumer market. Studies of their diagnostic accuracy are rare. We assessed the diagnostic accuracy of an open-access AI application (Skin Image Search™) for recognition of skin diseases. Clinical images including tumours, infective and inflammatory skin diseases were collected at the Department of Dermatology at the Sahlgrenska University Hospital and uploaded for classification by the online application. The AI algorithm classified the images giving 5 differential diagno-ses, which were then compared to the diagnoses made clinically by the dermatologists and/or histologically. We included 521 images portraying 26 diagnoses. The diagnostic accuracy was 56.4% for the top 5 suggested diagnoses and 22.8% when only considering the most probable diagnosis. The level of diagnostic accuracy varied considerably for diagnostic groups. The online application demonstrated low diagnostic accuracy compared to a dermatologist evaluation and needs further development.</p>}},
  author       = {{Zaar, Oscar and Larson, Alexander and Polesie, Sam and Saleh, Karim and Tarstedt, Mikael and Olives, Antonio and Suarez, Andrea and Gillstedt, Martin and Neittaanmäki, Noora}},
  issn         = {{0001-5555}},
  keywords     = {{Artificial intelligence; Dermato-logy; Online diagnostics; Skin disease}},
  language     = {{eng}},
  number       = {{16}},
  pages        = {{1--6}},
  publisher    = {{Medical Journals Limited}},
  series       = {{Acta Dermato-Venereologica}},
  title        = {{Evaluation of the diagnostic accuracy of an online artificial intelligence application for skin disease diagnosis}},
  url          = {{http://dx.doi.org/10.2340/00015555-3624}},
  doi          = {{10.2340/00015555-3624}},
  volume       = {{100}},
  year         = {{2020}},
}