Improved Detection of Lentigo Maligna with AI-Assisted Dermoscopy: A Reader Study in Facial Pigmented Lesions.

BACKGROUND: Differentiating lentigo maligna (LM) from benign facial pigmented lesions remains difficult due to substantial clinical and dermoscopic overlap, particularly on chronically sun-damaged skin, a setting underrepresented in existing AI studies. OBJECTIVE: To develop and evaluate a deep learning-based model for facial pigmented lesions and assess its impact on dermatology resident diagnostic performance. METHODS: In this retrospective study, 722 lesions (894 dermoscopic images) were analyzed (LM: 190; PAK: 230; SL/SK: 302). Twenty percent of lesions were reserved for testing; the remainder underwent 5-fold stratified cross-validation. An Xception-based convolutional neural network was trained for binary and 3-class classification. A reader study with 26 residents compared diagnostic accuracy before and after AI assistance. RESULTS: The model achieved a mean accuracy of 84.2% ± 2.5%, sensitivity of 90.8% ± 11.5%, and specificity of 81.9% ± 1.2%. Resident accuracy improved from 64.9% to 74.0% with AI support (p < 0.0001), with the largest gain observed in LM detection (+16.5%). LIMITATIONS: Retrospective design, lack of multimodal clinical data, and resident-only reader study. CONCLUSION: AI-assisted dermoscopy improves diagnostic performance in a challenging facial lesion setting, particularly for LM, supporting its role as an adjunct tool in clinical decision-making and dermatology training.