Background: Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. Objective: The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. Methods: A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. Results: We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. Conclusions: Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.

Background: The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. Methods: Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. Results: A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. Conclusion: AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.

Background: Patients with skin lesions suspicious for skin cancer or atypical melanocytic nevi of uncertain malignant potential often present to dermatologists, who may have variable dermoscopy triage clinical experience. Objective: To evaluate the clinical utility of a digital dermoscopy image-based artificial intelligence algorithm (DDI-AI device) on the diagnosis and management of skin cancers by dermatologists. Methods: Thirty-six United States board-certified dermatologists evaluated 50 clinical images and 50 digital dermoscopy images of the same skin lesions (25 malignant and 25 benign), first without and then with knowledge of the DDI-AI device output. Participants indicated whether they thought the lesion was likely benign (unremarkable) or malignant (suspicious). Results: The management sensitivity of dermatologists using the DDI-AI device was 91.1%, compared to 84.3% with DDI, and 70.0% with clinical images. The management specificity was 71.0%, compared to 68.4% and 64.9%, respectively. The diagnostic sensitivity of dermatologists using the DDI-AI device was 86.1%, compared to 78.8% with DDI, and 63.4% with clinical images. Diagnostic specificity using the DDI-AI device increased to 80.7%, compared to 75.9% and 73.6%, respectively. Conclusion: The use of the DDI-AI device may quickly, safely, and effectively improve dermoscopy performance, skin cancer diagnosis, and management when used by dermatologists, independent of training and experience.

The unprecedented onset of the COVID-19 crisis poses a significant challenge to all fields of medicine, including dermatology. Since the start of the coronavirus outbreak, a stark decline in new skin cancer diagnoses has been reported by countries worldwide. One of the greatest challenges during the pandemic has been the reduced access to face-to-face dermatologic evaluation and non-urgent procedures, such as biopsies or surgical excisions. Teledermatology is a well-integrated alternative when face-to-face dermatological assistance is not available. Teledermoscopy, an extension of teledermatology, comprises consulting dermoscopic images to improve the remote assessment of pigmented and non-pigmented lesions when direct visualisation of lesions is difficult. One of teledermoscopy’s greatest strengths may be its utility as a triage and monitoring tool, which is critical in the early detection of skin cancer, as it can reduce the number of unnecessary referrals, wait times, and the cost of providing and receiving dermatological care. Mobile teledermoscopy may act as a communication tool between medical practitioners and patients. By using their smartphone (mobile phone) patients can monitor a suspicious skin lesion identified by their medical practitioner, or alternatively self-detect concerning lesions and forward valuable dermoscopic images for remote medical evaluation. Several mobile applications that allow users to photograph suspicious lesions with their smartphones and have them evaluated using artificial intelligence technology have recently emerged. With the growing popularity of mobile apps and consumer-involved healthcare, this will likely be a key component of skin cancer screening in the years to come. However, most of these applications apply artificial intelligence technology to assess clinical images rather than dermoscopic images, which may lead to lower diagnostic accuracy. Incorporating the direct-to-consumer mobile dermoscopy model in combination with mole-scanning artificial intelligence as a mobile app may be the future of skin cancer detection.

Utilization of dermoscopy and novel molecular triage technologies augments visual triage of pigmented skin lesions, promoting early detection of melanoma. One emerging in vivo genomic test, 3‐GEP pigmented lesion assay (3‐GEP PLA) aids in pigmented lesion triage by noninvasively detecting the presence of three genes associated with melanoma: LINC00518, PRAME, and TERT. The purpose of our retrospective case‐control study was to identify dermoscopic features uniquely associated with the presence of LINC00518, PRAME, or TERT in the stratum corneum as determined by 3‐GEP PLA testing. Images of suspicious pigmented lesions that had undergone 3‐GEP PLA testing and received a definitive positive or negative result ( n = 393) were evaluated for the presence of specific clinical and dermoscopic features associated with melanoma. We found that asymmetry of color was a significant predictor for PRAME expression (Odds Ratio (OR) 5.5, 95% Confidence Interval (CI) 1.6–34.5, p = 0.004), blue color and negative pigment network were significant predictors for LINC00518 expression (adjusted OR 2.7, 95% CI 1.2–5.5, p = 0.014 and adjusted OR 5.4, 95% CI 1.6–16.9, p = 0.010, respectively), and atypical polymorphous vessels present in a pigmented skin lesion were a significant predictor for TERT promoter mutations (OR 5.8, 95% CI 1.3–23.4, p = 0.022). The results presented suggest a hierarchy in the significance of these dermoscopic features and may help guide evaluation and management of pigmented skin lesions.