Researchers from Fox Chase Cancer Center, Temple University’s College of Engineering, and the Lewis Katz School of Medicine at Temple University have developed an advanced artificial intelligence method that significantly improves the accuracy of skin cancer detection in people with darker skin tones. Current AI tools have historically struggled with diagnosing skin cancer in individuals with higher melanin levels, leading to disparities in early detection and treatment outcomes. This new approach addresses that gap by leveraging a more diverse and representative dataset, enabling AI models to better recognize subtle visual cues associated with skin lesions across a broader range of skin tones. The system was trained using high-resolution images from a wide demographic pool, helping reduce bias and enhance performance across different skin types. Early results show a marked improvement in sensitivity and specificity for detecting melanoma and other skin cancers in darker-skinned patients. The researchers emphasize that this advancement could help close longstanding disparities in dermatological care and improve early diagnosis rates, ultimately saving lives. The team plans to further validate the model in clinical settings and explore integration into digital health platforms for broader use.