The reasons for this exclusion are complex. According to Andrew Alexis, a dermatologist at Mount Sinai, in New York City, and the director of the Skin of Color Center, compounding factors include a lack of medical professionals from marginalized communities, inadequate information about those communities, and socioeconomic barriers to participating in research. “In the absence of a diverse study population that reflects that of the U.S. population, potential safety or efficacy considerations could be missed,” he says.

Adamson agrees, elaborating that with inadequate data, machine learning could misdiagnose people of color with nonexistent skin cancers—or miss them entirely. But he understands why the field of dermatology would surge ahead without demographically complete data. “Part of the problem is that people are in such a rush. This happens with any new tech, whether it’s a new drug or test. Folks see how it can be useful and they go full steam ahead without thinking of potential clinical consequences. What these folks [in the CNN trial] have done is they’ve gone after easily accessible data sets. But data sets are inherently biased.”

The ideal solution, then, would be to ensure a more equitable demographic participation in clinical trials, and in the case of machine learning, to save photo sets of skin conditions on diverse skin types for the algorithm to “learn” from. Adamson believes that the remedy “is not necessarily easy, but it is simple.”

Timo Buhl, a dermatologist at the University Medical Center Göttingen and a co-author of the CNN study, readily admits to the study’s demographic data gaps. “Most images in our study were taken of moles and melanomas of white people, which reflects the vast majority of patients here [in Germany],” he says. Buhl adds that he’s currently building data sets and running experiments with images from “other parts of the world.” The ISIC, too, is looking to expand its archive to include as many skin types as possible, according to Allan C. Halpern, a dermatologist at Memorial Sloan Kettering, in New York City, and a spokesperson for the organization.

Adamson wants dermatologists to begin actively contributing photos of lesions on their patients with darker skin tones to the open-source ISIC. Smith agrees, saying that contributions will be most valuable if they extend beyond the United States and Europe. “You have [dozens of] countries across the world with majority-black populations,” he says. “There needs to be more photos taken of their moles.”

Improving machine-learning algorithms is far from the only method to ensure that people with darker skin tones are protected against the sun and receive diagnoses earlier, when many cancers are more survivable. According to the Skin Cancer Foundation, 63 percent of African Americans don’t wear sunscreen; both they and many dermatologists are more likely to delay diagnosis and treatment because of the belief that dark skin is adequate protection from the sun’s harmful rays. And due to racial disparities in access to health care in America, African Americans are less likely to get treatment in time.

“These organizations are training their machine learning on Caucasian skin, so it ends up providing advancement for the population that has the highest survival rate,” Smith says.

“AI isn’t bad; quite the opposite,” Adamson adds. “I just think it should be inclusive.”

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