The Blind Spots of Artificial Intelligence in Skin Cancer Diagnosis

¹Department of Dermatology, Northwell Health, New Hyde Park, NY, USA

²Edward Via College of Osteopathic Medicine, Auburn, AL, USA

³New York Institute of Technology College of Osteopathic Medicine, Old Westbury, NY, USA

⁴Idaho College of Osteopathic Medicine, Meridian, ID, USA

*Corresponding author: Kelly Frasier, DO, MS, Department of Dermatology, Northwell Health, New Hyde Park, NY, USA, Phone: 3105956882, Email: [email protected]

Received Date: April 03, 2025

Publication Date: April 22, 2025

Citation: Frasier K, et al. (2025). The Blind Spots of Artificial Intelligence in Skin Cancer Diagnosis. Dermis. 5(2):35.

Copyright: Frasier K, et al. © (2025).

ABSTRACT

Artificial intelligence (AI) has emerged as a promising tool in the detection of skin cancer, particularly melanoma, through the use of deep learning algorithms trained on vast datasets of dermoscopic images. While early results suggest that AI systems can match or even exceed the diagnostic accuracy of dermatologists, significant limitations hinder their clinical integration and broader application. One major challenge remains the lack of diversity in training datasets, which limits AI’s efficacy in detecting skin cancers in individuals with darker skin tones, leading to potential disparities in diagnostic accuracy. Another limitation lies in the interpretability of AI decisions, as many deep learning algorithms function as “black boxes” with little transparency about how they reach specific conclusions. This lack of explainability can undermine physician trust and hinder the adoption of AI in clinical practice. Additionally, AI tools often struggle with rare or atypical presentations of skin cancer, which are underrepresented in the training datasets, increasing the risk of misdiagnosis. Ethical concerns include data privacy, informed consent, and the potential for AI systems to perpetuate biases if not adequately regulated. Furthermore, the lack of standardized protocols for integrating AI into clinical workflow presents operational challenges, such as determining when and how AI should be used as a decision-support tool versus a primary diagnostic method. Lastly, regulatory frameworks lag behind technological advancements, leading to uncertainty about the approval, oversight, and liability associated with AI-based diagnostic systems. Addressing AI’s limitations in skin cancer detection will require technological advancements and a concerted effort to improve dataset diversity, enhance model transparency, ensure ethical use, and develop standardized clinical protocols—ultimately ensuring that AI complements rather than compromises the future of dermatological care.

Keywords: Artificial Intelligence, Dermatology, Skin Cancer, Traditional Diagnostic Methods, Dermatological Care