Machine learning algorithms using dermatoscopy for the prediction of melanoma prognosis: A narrative review of the literature

Abstract

Introduction: Data about the role of dermatoscopy-based artificial intelligence (AI) models on melanoma prognosis remain scant. Methods: A comprehensive literature search was conducted in electronic databases until June 2025. Studies referring to machine learning algorithms (MLAs) trained on dermatoscopic images and analyzing the risk of melanoma metastasis were included. Also, due to scarcity of data regarding direct metastasis prediction, studies assessing melanoma prognostic factors, including Breslow thickness (BT), ulceration and histopathological subtype were analyzed. Results: 18 studies met the inclusion criteria. Two studies focused on metastasis prediction: a pre-trained ResNet50 demonstrated comparable accuracy to tumor prognostic factors, while a Foundation model achieved AUC 0.96 (95 %CI 0.93 – 0.99). 13 studies assessed MLAs for BT prediction, using clinically meaningful thresholds (0.8 or 2.0 mm). MLAs showed substantial accuracy in binary tasks, particularly when advanced models (i.e. semi-supervised learning), or sophisticated loss of function techniques were employed. However, the inherent complexity of multiclass tasks reduced performance, due to class imbalance and overlapping image features. Furthermore, despite explainable AI highlighted blue pigmentation and atypical vascular pattern indicative of thick melanomas, models’ performance constraints were also noticed. Across BT continuum, models struggled to classify correctly lesions with intermediate (0.4–1.1 mm) or very thick melanomas. Finally, 3 studies evaluated MLAs for histopathological subtype recognition, with transfer learning achieving high accuracy across superficial spreading, nodular and acral subtypes. Outcomes: MLAs based on dermatoscopy demonstrated feasibility for predicting metastasis and prognostic factors of melanoma preoperatively. Future studies integrating this into multimodal frameworks, including digital pathology and gene signatures, seems important.

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