Artificial Intelligence for Skin Cancer Imaging: Performance, Translational Gaps, and a Three-Tier Clinical Readiness Framework

Abstract

Artificial Intelligence (AI)-driven image analysis has made significant strides in dermatologic cancer care, showing dermatologist-level accuracy in melanoma detection through various publications. However, its integration into routine clinical practice remains limited, resulting in minimal impact in real-world scenarios. This review assesses AI applications across three main imaging methods in skin cancer management: clinical photography, dermoscopy, and histopathological slides. Key factors affecting diagnostic efficacy include image quality, annotation methods, dataset composition, and deployment context. Convolutional neural networks, particularly when provided with high-resolution images and validated biopsy labels, demonstrate effectiveness comparable to skilled dermatologists. However, performance diminishes significantly when models are tested on diverse devices, skin types, or lesion types. Clinical photograph systems, while less accurate, offer advantages in scalable triage and teledermatology. Histopathological models produce robust confirmatory results; however, they face obstacles including inadequate supervision, variability in staining, and substantial computational demands. Common issues across these modalities include dataset bias, underrepresentation of darker skin tones and benign lesions, noisy ground truth, and poor integration into clinical workflows. The authors put forward a three-tier framework to get the medical field ready for AI. They suggest using it in primary care to screen for lesions, as a decision-support tool in dermoscopy, and as a computational biomarker in digital pathology. Future developments will hinge on creating representative datasets, advancing training techniques, ensuring interpretability, long term monitoring of lesions, and achieving regulatory validation. Ultimately, mature AI systems are anticipated to act as diagnostic collaborators rather than replacements for clinicians, focusing on reducing unnecessary procedures, standardizing biopsy criteria, and improving equity in skin cancer management.