Background: Basal cell carcinoma (BCC) is the most common skin cancer, requiring an early diagnosis and accurate margin definition to prevent functional and cosmetic complications. Traditional methods using clinical and dermoscopic images (C&D) often rely on biopsies and histology for final validation. Non‐invasive techniques like LC‐OCT, enabling ‘digital biopsies’, are promising alternatives, but remain underutilized due to the expertise required. The development of Artificial Intelligence (AI) algorithms is a promising approach to assist dermatologists in their diagnosis and support the broader adoption of such technologies. Objectives: We present a real‐time AI assistant for BCC diagnosis with LC‐OCT, which is, to date, the only real‐time AI model across all dermatological imaging modalities. The study aims to quantify the model's effectiveness when used by dermatologists with different levels of expertise and compare its performance with traditional methods and unaided LC‐OCT. Methods: This multicenter, retrospective study involved 43 dermatologists in a double‐rounded quiz on 200 equivocal BCC lesions. Diagnoses were first made on C&D images, then with LC‐OCT or AI‐assisted LC‐OCT in a randomized manner. Results: AI‐assisted LC‐OCT significantly improves dermatologists' diagnostic performance in detecting BCC (+25.8 points in sensitivity and +16.8 points in specificity compared to C&D), particularly benefiting those with less LC‐OCT experience, effectively bridging a 2‐year gap of expertise. These results highlight the potential for broader clinical adoption through AI assistance and underscore its promise in reducing the need for invasive procedures and improving patient outcomes. Conclusions: These results support a broader adoption of LC‐OCT use in clinical practice thanks to AI assistance and underscore its promise in reducing the need for invasive procedures and improving patient outcomes.

Background: There is currently no staging system for cutaneous squamous cell carcinoma (cSCC) that is adapted to decision‐making and universally used. Experts have unconscious ability to simplify the heterogeneity of clinical situations into a few relevant groups to drive their therapeutic decisions. Therefore, we have used unsupervised clustering of real cases by experts to generate an operational classification of cSCCs, an approach that was successful for basal cell carcinomas.Objectives: To generate a consensual and operational classification of cSCCs.Methods: Unsupervised independent clustering of 248 cases of cSCCs considered difficult‐to‐treat. Eighteen international experts from different specialties classified these cases into what they considered homogeneous clusters useful for management, each with freedom regarding clustering criteria. Convergences and divergences between clustering were analysed using a similarity matrix, the K‐mean approach and the average silhouette method. Mathematical modelling was used to look for the best consensual clustering. The operability of the derived classification was validated on 23 new practitioners.Results: Despite the high heterogeneity of the clinical cases, a mathematical consensus was observed. It was best represented by a partition into five clusters, which appeared a posteriori to describe different clinical scenarios. Applicability of this classification was shown by a good concordance (94%) in the allocation of cases between the new practitioners and the 18 experts. An additional group of easy‐to‐treat cSCC was included, resulting in a six‐group final classification: easy‐to‐treat/complex to treat due to tumour and/or patient characteristics/multiple/locally advanced/regional disease/visceral metastases.Conclusions: Given the methodology based on the convergence of unguided intuitive clustering of cases by experts, this new classification is relevant for clinical practice. It does not compete with staging systems, but they may complement each other, whether the objective is to select the best therapeutic approach in tumour boards or to design homogeneous groups for trials.

Background: Line-field confocal optical coherence tomography (LC-OCT) is an innovative non-invasive imaging technique recently introduced in dermatology. Its use has been established particularly in the diagnosis of skin cancer. The LC-OCT description of basal cell carcinoma (BCC) criteria has served for the conduction of retrospective evaluations on its diagnostic performance, which demonstrated its utility in diagnosis and subtyping BCC. However, prospective studies performed at patients’ bedside are currently lacking in this field. Objective: The objective of the study was to provide parameters of LC-OCT diagnostic performance for BCC derived from a prospective study performed at patients’ bedside. Methods: Lesions clinically equivocal for BCC were prospectively included. Dermoscopic and LC-OCT diagnoses were obtained at the patients’ bedside by an expert observer prior to surgical excision or biopsy. Results: A total of 214 lesions (163 BCCs and 51 BCC imitators) belonging to 119 patients were included. For the differentiation of BCC from BCC-imitators, LC-OCT had the same sensitivity as dermoscopy (98%) but a better specificity (90% vs. 37%). For the discrimination of superficial BCC from other BCC subtypes, LC-OCT had an increased sensitivity and specificity compared to dermoscopy (72% vs. 62%, and 97% vs. 84%, respectively). Conclusions: This prospective study showed that the diagnostic performance for BCC diagnosis and subtyping can be remarkedly increased by LC-OCT compared to dermoscopic examination alone. Our data encourages the inclusion of LC-OCT in the diagnostic process and management of equivocal lesions for BCC.

Extramammary Paget disease (EMPD) is an uncommon adenocarcinoma of apocrine gland-rich areas, presenting significant diagnostic challenges due to its nonspecific clinical appearance and frequent misidentification as benign, inflammatory skin conditions. Traditional diagnostic methods such as biopsy are invasive and uncomfortable, often required repeatedly due to high recurrence rates. Dermoscopy and non-invasive imaging techniques have been used but provide limited diagnostic accuracy due to their constraints in depth penetration and resolution. Recent advancements in imaging technologies, such as line-field confocal optical coherence tomography (LC-OCT), show promise in enhancing diagnostic precision while minimizing invasive procedures. LC-OCT merges high-resolution imaging with deep penetration capabilities, capturing detailed horizontal and vertical skin images akin to histopathology. This study evaluated the diagnostic performance of LC-OCT in detecting EMPD and its recurrence in 17 clinically suspicious anogenital regions, belonging to six patients. Data were collected prospectively at the patient’s bedside by an LC-OCT expert with poor training for EMPD, and, then, reviewed retrospectively by an independent LC-OCT expert with adequate training for EMPD and no concerns about time. The prospective examination yielded 64.7% accuracy (11 true results out of 17 total cases), 71.4% sensitivity (10 true positives out of 14 actual positives), and 33.3% specificity (1 true negative out of 3 actual negatives). The retrospective analysis achieved 94.1% accuracy (16 true results out of 17 total cases), 100% sensitivity (14 true positives out of 14 actual positives), and 66.7% specificity (2 true positives out of 3 actual positives), with the only false positive case being a difficult-to-diagnose concomitant presentation of a lichen sclerosus et atrophicus. Despite the need for specialized training, our results suggest that LC-OCT represents a valuable tool for accurately identifying EMPD and improving its management by reducing unnecessary biopsies. Further studies are needed to standardize its clinical application.