aceMelanoma Prognostication Using AI-Guided Histopathology
Accurate diagnosis and risk stratification are central for optimal management of patients with melanoma. Current American Joint Committee on Cancer (AJCC) staging systems inadequately stratify patients with clinically meaningful metastatic potential. Manual histopathologic interpretation compounds this limitation, particularly for diagnostically ambiguous lesions at the benign-malignant interface, where concordance is highly variable. This review examines how computational pathology and convolutional neural netw…
Deep learning models applied to H&E whole-slide images can reliably identify melanomas at risk of recurrence and progression, improving diagnosis and personalized risk stratification for stage I-III cutaneous melanoma.
Dataset diversity, external validation, interpretability, and generalizability limitations across populations and imaging protocols currently prevent clinical adoption of AI histopathology tools for melanoma.
Clinical adoption is limited by dataset diversity, lack of external validation, model interpretability, and poor generalizability across populations and image acquisition protocols.
Evidence
- Peer-reviewedInternational Journal of Dermatology2026-10-01
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Truvace Impact Record TRV-2026-1247, v1: “Melanoma Prognostication Using AI-Guided Histopathology.” Truvace, 2026-10-02. /record/TRV-2026-1247 (accessed at citation time). sha256 6f30b9ca5d0ce937…
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