Dermatology is a hot space in digital health. Even Google is creating tools to help patients find answers to dermatological questions. However, there are still many questions about how artificial intelligence algorithms are trained, and whether the data sets used are representative of the population.
New research out of the Lancet found that publicly available datasets that are used to train skin cancer diagnosis algorithms are lacking data surrounding ethnicity and skin type.
“Although this represents a rich data resource for innovation, lack of transparency in metadata reporting for clinically essential characteristics (such as ethnicity and Fitzpatrick skin type) limits the clinical utility of these images alone. These issues are not limited to dermatology datasets, but have also been reported in ophthalmology and radiology,” authors of the research wrote.
Researchers combined data sets from MEDLINE, Google, and Google Dataset Search and found 21 open-access data sets that contained 106,950 skin-lesion images. However, overall information about ethnicity and skin type was limited. Patient ethnicity was …