For AI to work fairly in healthcare, it needs to be trained on data that represents people from different parts of the world. This study, carried out in Uganda, tested how well an AI app called Skin Image Search, which suggests possible skin conditions from a photo, performed on people with very dark skin (Fitzpatrick skin type 6).
The researchers analysed 123 images of skin conditions from a Ugandan telehealth company, 62% of them from women and 38% from men. The app’s overall diagnostic accuracy was only 17%, far below the 69.9% reported in its training results on white skin. Performance varied between conditions and was highest for dermatitis (80%).
The authors conclude that the images used to train dermatology algorithms need to be more diverse, so that these tools are accurate across skin types and geographies.
Learn more about this study here: https://doi.org/10.4314/ahs.v23i2.86
Reference:
Kamulegeya, L., Bwanika, J., Okello, M., Rusoke, D., Nassiwa, F., Lubega, W., Musinguzi, D., & Börve, A. (2023). Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning. African Health Sciences, 23(2), 753–763.