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 AIbased 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.