Background: Line-field confocal optical coherence tomography is a novel technology able to reproduce a “virtual biopsy” of the skin. The aim of this review is to explore the application of line-field confocal optical coherence tomography (LC-OCT) in various skin diseases, covering skin cancers, inflammatory and infectious skin diseases, genetic diseases, cosmetic procedures, and less common disorders. Methods: Study selection was conducted based on LC-OCT and using pertinent MeSh terms, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines from inception to March 2024; to evaluate the quality and risk of bias of studies, Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used. Results: the search retrieved 154 papers according to the selection criteria; after removing publications by one or more of the exclusion criteria, a total of 96 studies were found to be suitable for the analysis. Conclusions: Increasing evidence supports the use of LC-OCT as an adjunctive diagnostic tool for the in vivo diagnosis of a variety of skin tumors. As this device can be considered a “bridge” between dermoscopy and histopathology, widening applications in numerous fields of clinical dermatology, including inflammatory skin disease treatment, presurgical mapping, cosmetic procedures, and monitoring of non-invasive therapies, have been explored.

Background: Reflectance confocal microscopy (RCM) and line‐field confocal optical coherence tomography (LC‐OCT) are non‐invasive imaging devices that can help in the clinical diagnosis of actinic keratosis (AK) and cutaneous squamous cell carcinoma (SCC). No studies are available on the comparison between these two technologies for the identification of the different features of keratinocyte skin tumours.Objectives: To compare RCM and LC‐OCT findings in AK and SCC.Methods: A retrospective multicenter study was conducted. Tumours were imaged with RCM and LC‐OCT devices before surgery, and the diagnosis was confirmed by histological examinations. LC‐OCT and RCM criteria for AK/SCC were identified, and their presence/absence was evaluated in all study lesions. Gwet AC1 concordance index was calculated to compare RCM and LC‐OCT.Results: We included 52 patients with 33 AKs and 19 SCCs. Irregular epidermis was visible in most tumours and with a good degree of agreement between RCM and LC‐OCT (Gwet's AC1 0.74). Parakeratosis, dyskeratotic keratinocytes and both linear dilated and glomerular vessels were better visible at LC‐OCT than RCM (p < 0.001). Erosion/ulceration was identified with both methods in more than half of the cases with a good degree of agreement (Gwet AC1 0.62).Conclusions: Our results suggest that both LC‐OCT and hand‐held RCM can help clinicians in the identification of AK and SCC, providing an in vivo and non‐invasive identification of an irregular epidermis. LC‐OCT proved to be more effective in identifying parakeratosis, dyskeratotic keratinocytes and vessels in this series.

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.