Using artificial intelligence on dermatology conditions in Uganda: a case for diversity in training data sets for machine learning

Background: In pursuit of applying universal non-biased Artificial Intelligence (AI) in healthcare, it is essential that data from different geographies are represented. Objective: To assess the diagnostic performance of an AI-powered dermatological algorithm called Skin Image Search on Fitzpatrick6...

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Vydané v:African health sciences Ročník 23; číslo 2; s. 753 - 63
Hlavní autori: Kamulegeya, Louis, Bwanika, John, Okello, Mark, Rusoke, Davis, Nassiwa, Faith, Lubega, William, Musinguzi, Davis, Börve, Alexander
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Uganda Makerere Medical School 01.06.2023
Predmet:
Artificial Intelligence
Dermatology
Female
Humans
Machine Learning
Male
Retrospective Studies
Uganda
Uganda
Algorithms
Fitzpatrick 6 skin type
Telehealth
Dermatology
Artificial intelligence
ISSN:1680-6905, 1729-0503, 1729-0503
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Shrnutí:Background: In pursuit of applying universal non-biased Artificial Intelligence (AI) in healthcare, it is essential that data from different geographies are represented. Objective: To assess the diagnostic performance of an AI-powered dermatological algorithm called Skin Image Search on Fitzpatrick6 skin type (dark skin) dermatological conditions. Methods: 123 dermatological images selected from a total of 173 images were retrospectively extracted from the electronic database of a Ugandan telehealth company, The Medical Concierge Group (TMCG) after getting their consent. Details of age, gender, and dermatological clinical diagnosis were analysed using R on R studio software to assess the diagnostic accuracy of the AI app along with disease diagnosis and body part. Predictability levels of the AI app were graded on a scale of 0 to 5, where 0- no prediction was made and 1-5 demonstrated a reduction incorrect diagnosis prediction rate of the AI. Results: 76 (62%) of the dermatological images were from females and 47 (38%) from males. Overall diagnostic accuracy of the AI app on black dermatological conditions was low at 17% (21 out of 123 predictable images) compared to 69.9% performance on Caucasian skin type as reported from the training results. There were varying predictability levels correctness i.e., 1-8.9%, 2-2.4%, 3-2.4%, 4-1.6%, 5-1.6% with performance along individual diagnosis highest with dermatitis (80%). Conclusion: There is need for diversity of image datasets used to train dermatology algorithms for AI applications to increase accuracy across skin types and geographies. Keywords: Artificial intelligence; Dermatology; Fitzpatrick 6 skin type; Telehealth; Algorithms.
Bibliografia:ObjectType-Article-1
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ISSN:1680-6905
1729-0503
1729-0503
DOI:10.4314/ahs.v23i2.86