The Blind Spots of Artificial Intelligence in Skin Cancer Diagnosis

INTRODUCTION Artificial intelligence (AI) has gained significant attention for its potential to revolutionize the field of dermatology, particularly in the detection and diagnosis of skin cancer. Skin cancer, including melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), is one of the most common cancers worldwide [1]. Melanoma is the deadliest, responsible for an estimated 57,000 deaths worldwide in 2020 [2]. Early detection is critical in improving survival rates, as melanoma’s prognosis significantly worsens once it metastasizes. Studies show that localized melanoma has a five-year survival rate of approximately 99% when detected early. However, once melanoma spreads to regional lymph nodes or distant organs, the five-year survival rate drops significantly to 75% and 35%, respectively [3]. Traditional diagnostic methods, which rely on visual inspection, dermoscopy, and biopsy, are often limited by clinician expertise, inter-observer and intra-observer variability, and time constraints [4]. AI, specifically deep learning algorithms, has emerged as a promising tool to enhance accuracy and efficiency in skin cancer detection by rapidly analyzing dermoscopic images.