Abstract Search

ISEF | Projects Database | Finalist Abstract

Back to Search Results | Print PDF

Using Machine Learning To Detect and Prevent Early Stages of Skin Cancer in Underrepresented Communities

Booth Id:
TMED020

Category:
Translational Medical Science

Year:
2022

Finalist Names:
Nordstrom, Emma (School: Trinity Lutheran School)

Abstract:
Artificial intelligence is becoming a more widely utilized technology in the healthcare industry to improve predictability and consistency. However, this powerful technology does come with some limitations, one being the datasets used usually neglect people of color. This is problematic because the people most affected by skin disease are those of color. Skin cancer in these communities is often detected in the late stages. This is caused by skin tone, shortage of dermatologists, and cost of skin checks. The model I developed had an overall accuracy of 69.33%. The accuracy my model produced is extremely promising and has the potential to save lives. The capabilities of artificial intelligence could revolutionize the dermatology industry by making it more accessible for people to get tested for skin diseases.