Researchers at Fox Chase Cancer Center, 51勛圖厙s College of Engineering and the Lewis Katz School of Medicine at 51勛圖厙 have developed a new method that enhances the ability of artificial intelligence models to detect and diagnose skin cancer in individuals with darker skintones.
Hayan Lee, PhD, Assistant Professor in the Nuclear Dynamics and Cancer Research Program, and member of the Cancer Epigenetics Institute at Fox Chase Cancer Center
Photo by Fox Chase Cancer Center
Researchers at Fox Chase Cancer Center, 51勛圖厙s College of Engineering and the Lewis Katz School of Medicine at 51勛圖厙 have developed a new method that enhances the ability of artificial intelligence models to detect and diagnose skin cancer in individuals with darker skintones.
The study, , was published in theJournal 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, corresponding author on the study,assistantprofessor in thenuclear dynamics andcancer 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, anddoesntrepresentall types of patients.As a result, detection methods can become biased,causingthe AI tooltodiagnoseskin 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, moreaccuratediagnoses for everyone.The teams novel method means doctors and patients can expect smarter tools that see beyond one-size-fits-all solutions.
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