Gender Disparities in Melanoma: Advances in Diagnosis, Treatment, and the Role of Artificial Intelligence

Abstract

Background

Melanoma, a highly aggressive skin cancer, demonstrates significant gender disparities, with men facing later-stage diagnoses, more aggressive tumor characteristics, and worse survival rates. This review examines the biological, behavioral, and environmental factors driving these disparities, alongside recent advancements in diagnosis and treatment. Additionally, it explores how artificial intelligence (AI) can address gender-specific differences in melanoma incidence and outcomes.

Results

Gender disparities in melanoma stem from biological factors, such as hormonal and genetic differences, and behavioral patterns like delayed health-seeking among men. AI-driven diagnostic tools, including convolutional neural networks (CNNs), show promise but often reflect biases in training data sets, underrepresenting darker skin tones and gender-specific patterns. Ensuring diverse data sets, integrating “super-prompts” or region-specific demographic prompts, and utilizing bias-aware algorithms can help mitigate these biases, thereby improving diagnostic accuracy and equity.

Conclusion

Reducing gender disparities in melanoma requires integrating innovative technologies with equitable healthcare policies and education. Early detection using inclusive AI models tailored to diverse skin tones and genders, alongside targeted therapeutic strategies, is critical to improving outcomes for high-risk groups, particularly men and underserved populations.