aceArtificial Intelligence applied to surgical pathology in the management of cutaneous melanoma
Surgical pathology remains the cornerstone for the diagnosis, staging and prognostic stratification of cutaneous melanoma. The emergence of digital pathology and artificial intelligence (AI), notably deep learning, is transforming this field. Convolutional neural networks trained on large repositories of digitised histological slides demonstrate diagnostic capabilities comparable, and in specific tasks superior, to board-certified dermatopathologists. Current applications encompass the automated classification o…
Convolutional neural networks trained on digitised slides can classify melanocytic lesions and infer molecular features such as BRAF status from routine stained slides, with performance comparable to dermatopathologists.
Use of pathology AI in melanoma is held back by dataset bias, inconsistent slide-imaging workflows between laboratories, limited model interpretability, and unresolved medico-legal accountability.
The source is a narrative overview, and it names unresolved issues including dataset bias, lab-to-lab slide workflow standardisation, interpretability and medico-legal accountability that limit routine adoption.
Evidence
- Peer-reviewedLa Presse Médicale2026-10-01
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Truvace Impact Record TRV-2026-1436, v1: “Artificial Intelligence applied to surgical pathology in the management of cutaneous melanoma.” Truvace, 2026-10-15. /record/TRV-2026-1436 (accessed at citation time). sha256 0306368df9812782…
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