Researchers at Fox Chase Cancer Center, Temple University’s College of Engineering and the Lewis Katz School of Medicine at Temple University have developed a new method that enhances the ability of artificial intelligence models to detect and diagnose skin cancer in individuals with darker skin tones.

Researchers at Fox Chase Cancer Center, Temple University’s College of Engineering and the Lewis Katz School of Medicine at Temple University have developed a new method that enhances the ability of artificial intelligence models to detect and diagnose skin cancer in individuals with darker skin tones. 

The study, “MST-AI: Skin Color Estimation in Skin Cancer Datasets,” was published in the Journal of Imaging. 

“The biggest issue with current AI cancer detection models is that they are more effective at detecting melanoma in lighter skin tones and often have difficulty detecting it in darker skin tones. As a result, when melanoma is detected in patients with darker skin, those patients tend to be diagnosed at later stages,” said Hayan Lee, corresponding author on the study, assistant professor in the nuclear dynamics and cancer research program, and member of the Cancer Epigenetics Institute at Fox Chase. 

According to the researchers, existing AI models are not as effective at detecting melanoma in dark skin because of the kinds of data used to train them. This data often comes from just a few places and time periods, many times in one country, and doesn’t represent all types of patients. As a result, detection methods can become biased, causing the AI tool to diagnose skin cancer more accurately in people with lighter skin tones than in people of color. 

By making sure AI has data on a wider range of skin tones, this research aims to close the gap in skin cancer detection and provide earlier, more accurate diagnoses for everyone. The team’s novel method means doctors and patients can expect smarter tools that see beyond one-size-fits-all solutions. 

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