The Impact of Artificial Intelligence on Early Detection of Skin Cancer

Abstract

Background: Early detection of skin cancer, particularly melanoma, is critical for improving patient outcomes and survival rates. Traditional diagnostic methods, such as visual inspection and biopsy, are time-consuming and may result in false positives or missed diagnoses. The integration of Artificial Intelligence (AI) in dermatology has shown promise in enhancing diagnostic accuracy, efficiency, and early detection of skin cancer. This study aims to evaluate the impact of AI technologies on the early detection of skin cancer compared to conventional methods.
Methods: A retrospective observational study was conducted with 200 patients (aged 18–80 years) suspected of having skin cancer, who underwent both AI-based diagnostic systems and traditional clinical evaluations (dermatologist inspection and biopsy). Diagnostic performance was assessed by evaluating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy.
Results: AI-based systems demonstrated 92.5% sensitivity, 88.3% specificity, 85.7% PPV, 94.2% NPV, and 90.1% accuracy. In comparison, traditional clinical methods showed 80.5% sensitivity, 75.4% specificity, 70.2% PPV, 84.1% NPV, and 77.6% accuracy. AI systems outperformed traditional methods in early melanoma detection, particularly in recognizing atypical nevi and skin lesions.
Conclusion: AI-based diagnostic tools significantly improved the accuracy of early skin cancer detection, with higher sensitivity, specificity, and overall diagnostic performance. AI technologies should be considered as a complementary tool to assist dermatologists in the early diagnosis of skin cancer, especially in resource-limited settings or large-scale screening programs.