‘We need more images of everybody’
Dermatologist Adewole Adamson, MD, MPP, assistant professor in the department of internal medicine (division of dermatology) at the University of Texas at Austin, said in an interview that a “major potential downside” of algorithms not trained on diverse datasets is the potential for incorrect diagnoses.
“The harms of algorithms used for diagnostic purposes in the skin can be particularly significant because of the scalability of this technology. A lot of thought needs to be put into how these algorithms are developed and tested,” said Dr. Adamson, who reviewed the manuscript of The Lancet Digital Health study but was not involved with the research.
He referred to the results of a recently published study in JAMA Dermatology, which found that only 10% of studies used to develop or test deep-learning algorithms contained metadata on skin tone. “Furthermore, most datasets are from countries where darker skin types are not represented. [These] algorithms therefore likely underperform on people of darker skin types and thus, users should be wary,” Dr. Adamson said.
A consensus guideline should be developed for public AI algorithms, he said, which should have metadata containing information on sex, race/ethnicity, geographic location, skin type, and part of the body. “This distribution should also be reported in any publication of an algorithm so that users can see if the distribution of the population in the training data mirrors that of the population in which it is intended to be used,” he added.
Adam Friedman, MD, professor and chair of dermatology at George Washington University, Washington, who was not involved with the research, said that, while this issue of underrepresentation has been known in dermatology for some time, the strength of the Lancet study is that it is a large study, with a message of “we need more images of everybody.”
“This is probably the broadest study looking at every possible accessible resource and taking an organized approach,” Dr. Friedman said in an interview. “But I think it also raises some important points about how we think about skin tones and how we refer to them as well with respect to misusing classification schemes that we currently have.”
While using ethnicity data and certain Fitzpatrick skin types as a proxy for darker skin is a limitation of the metadata the study authors had available, it also highlights “a broader problem with respect to lexicon regarding skin tone,” he explained.
“Skin does not have a race, it doesn’t have an ethnicity,” Dr. Friedman said.
A dataset that contains not only different skin tones but how different dermatologic conditions look across skin tones is important. “If you just look at one photo of one skin tone, you missed the fact that clinical presentations can be so polymorphic, especially because of different skin tones,” Dr. Friedman said.
“We need to keep pushing this message to ensure that images keep getting collected. We [need to] ensure that there’s quality control with these images and that we’re disseminating them in a way that everyone has access, both from self-learning, but also to teach others,” said Dr. Friedman, coeditor of a recently introduced dermatology atlas showing skin conditions in different skin tones.
Adamson reports no relevant financial relationships. Dr. Friedman is a coeditor of a dermatology atlas supported by Allergan Aesthetics and SkinBetter Science. This study was funded by NHSX and the Health Foundation. Three authors reported being paid employees of Databiology at the time of the study. The other authors reported no relevant financial relationships.
A version of this article first appeared on Medscape.com.