Advancing Melanoma Diagnostics: AI and Digital Pathology Synergy

A recent MDPI review found that AI-enabled digital pathology is improving melanoma diagnostic accuracy and reshaping pathology workflows.

Narrative syntheses summarize comparative performance across classification, feature extraction, and spatial‑recognition tasks, reporting pooled sensitivities and specificities in the high 80s–low 90s and AUCs up to 0.96–0.98 in controlled cohorts.

Convolutional neural networks add value through automated feature extraction, multiscale pattern recognition, and spatial modeling of tumor microarchitecture from whole‑slide images. Typical inputs are whole‑slide images with patch‑level annotation; outputs are probability scores, region heat maps, and segmentation masks.

The review notes improved sensitivity, specificity, and reduced interobserver variability when algorithms are used as adjuncts, and interpretability aids (saliency maps and attention overlays) can increase clinician trust while preserving oversight.

AI‑enhanced digital pathology offers promise but also clear prerequisites. High retrospective accuracy exists, but prospective multicenter validation and workflow readiness are essential.