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

Oscar Zaar, Alexander Larson, Sam Polesie, Karim Saleh, Mikael Tarstedt, Antonio Olives, Andrea Suarez, Martin Gillstedt, Noora Neittaanmäki

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numberadv00260
Pages (from-to)1-6
Number of pages6
JournalActa Dermato-Venereologica
Issue number16
Publication statusPublished - 2020

Subject classification (UKÄ)

  • Dermatology and Venereal Diseases

Free keywords

  • Artificial intelligence
  • Dermato-logy
  • Online diagnostics
  • Skin disease


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