Riya Bhat - Department of Dermatology
Melanoma, a highly aggressive form of skin cancer, requires early detection for improved treatment and survival rates. Traditionally, diagnosis involves clinical examination and biopsy, but reliance on specialists for detection has hindered those most in need from receiving timely care. These concerns have prompted advancements in alternative forms of care such as self-screenings, smartphone apps, artificial intelligence (AI), image analysis, and teledermatology.
In a review of melanoma detection methods by Garrison et. al., it was found that although these alternative methods have not yet led to significant improvements in survival outcomes for melanoma, they have contributed to a heightened sense of urgency among non-specialists, leading to more accurate referrals and better management of suspicious lesions.
Looking to the future, AI technologies and deep learning models, while still in their infancy, have shown promise in aiding dermatologists with their diagnosis. However, it is crucial to recognize their limitations. These models are often trained on datasets that may not fully represent the diversity of skin tones and types found in the general population, which can lead to biases in diagnosis. Additionally, the accuracy of AI-driven tools can be compromised by variations in image quality and the complexity of certain cases that require nuanced clinical judgment. Therefore, while AI has the potential to support dermatologists, it should be used as an adjunct to, rather than a replacement for, traditional diagnostic methods.
References:
Garrison ZR, Hall CM, Fey RM, et al. Advances in Early Detection of Melanoma and the Future of At-Home Testing. Life (Basel). 2023;13(4):974. Published 2023 Apr 9. doi:10.3390/life13040974
Edited by AJ Jenkins - Surgical Section Editor
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