Annals of Medical and Surgical Dermatology

ABSTRACT

Background: Skin cancers, including basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma, represent a growing global health challenge. Early and accurate diagnosis is crucial to improving clinical outcomes and reducing treatment costs. Dermoscopy is increasingly recognized as a valuable imaging tool that enhances the visualization of subsurface lesion features beyond the naked eye. The integration of artificial intelligence (AI) with dermoscopy has the potential to revolutionize diagnostic precision and efficiency, especially in resource-limited settings such as Syria.
Methods: This prospective diagnostic accuracy study evaluated the performance of an AI-assisted dermoscopy system compared to dermatologists and histopathology as the gold standard.
Results: A total of 115 lesions from 108 patients were analyzed. The AI demonstrated a 92% overall accuracy, surpassing dermatologist sensitivity and specificity. These findings underscore the transformative role of AI-augmented dermoscopy in dermatological diagnostics in developing countries.
Limitations: Several limitations warrant mention. The relatively small sample size, especially for SCC and melanoma cases, restricts broader generalization. Additional multicentric studies with diverse ethnic populations and lesion types are necessary to validate these results. Ethical considerations, including patient data privacy and AI decision transparency, must be addressed before clinical deployment. Objective: To highlight and evaluate the crucial role of combining dermoscopy with artificial intelligence (AI), specifically using the FotoFinder system in enhancing the diagnostic accuracy of skin cancers (including basal cell carcinoma, squamous cell carcinoma, and melanoma) in a Syrian clinical population. The study aims to demonstrate how integrating dermoscopy and AI can improve the sensitivity and specificity of skin cancer diagnosis compared to standard clinical evaluation, thus enabling earlier and more precise detection and treatment, with a focus on real-world application in dermatological practice.

KEYWORDS

1. Artificial intelligence (AI)
2. Dermoscopy
3. Skin cancer diagnosis
4. Basal cell carcinoma
5. Squamous cell carcinoma
6. Melanoma