Artificial intelligence (AI) has wide applications in healthcare, including dermatology. Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can progressively learn from data to predict the characteristics of new samples and perform a desired task. Although it has a significant role in the detection of skin cancer, dermatology skill lags behind radiology in terms of AI acceptance. With continuous spread, use, and emerging technologies, AI is becoming more widely available even to the general population. AI can be of use for the early detection of skin cancer. For example, the use of deep convolutional neural networks can help to develop a system to evaluate images of the skin to diagnose skin cancer. Early detection is key for the effective treatment and better outcomes of skin cancer. Specialists can accurately diagnose the cancer, however, considering their limited numbers, there is a need to develop automated systems that can diagnose the disease efficiently to save lives and reduce health and financial burdens on the patients. ML can be of significant use in this regard. In this article, we discuss the fundamentals of ML and its potential in assisting the diagnosis of skin cancer.

Dermatophytic infections of the skin and appendages are a common occurrence. The pathogenesis involves complex interplay of agent (dermatophytes), host (inherent host defense and host immune response) and the environment. Infection management has become an important public health issue, due to increased incidence of recurrent, recalcitrant or extensive infections. Recent years have seen a significant rise in incidence of chronic infections which have been difficult to treat. In this review, we review the literature on management of dermatophytoses and bridge the gap in therapeutic recommendations.

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.

Introduction: Ever evolving research in medical field has reached an exciting stage with advent of newer technologies. With the introduction of digital microscopy, pathology has transitioned to become more digitally oriented speciality. The potential of artificial intelligence (AI) in dermatopathology is to aid the diagnosis, and it requires dermatopathologists' guidance for efficient functioning of artificial intelligence.Method: Comprehensive literature search was performed using electronic online databases “PubMed” and “Google Scholar.” Articles published in English language were considered for the review.Results: Convolutional neural network, a type of deep neural network, is considered as an ideal tool in image recognition, processing, classification, and segmentation. Implementation of AI in tumor pathology is involved in the diagnosis, grading, staging, and prognostic prediction as well as in identification of genetic or pathological features. In this review, we attempt to discuss the use of AI in dermatopathology, the attitude of patients and clinicians, its challenges, limitation, and potential opportunities in future implementation.

Merkel cell carcinoma (MCC) is an infrequent, rapidly growing skin neoplasm that carries a greater probability of regional lymph node involvement, and a grim prognosis in advanced cases. While it is seen predominantly in old age in sun-exposed body parts, the prevalence varies among different races and geographical regions. Merkel cell polyomavirus and UV radiation-induced mutations contribute to its etiopathogenesis. The clinical presentation of MCC lacks pathognomonic features and is rarely considered highly at the time of presentation. Histopathological examination frequently reveals hyperchromatic nuclei with high mitotic activity, but immunohistochemistry is required to confirm the diagnosis. Sentinel lymph node biopsy (SLNB) and imaging are advised for effective staging of the disease. Multimodal management including surgery, radiation therapy, and/or immunotherapy are deployed. Traditional cytotoxic chemotherapies may result in an initial response, but do not result in a significant survival benefit. Checkpoint inhibitors have dramatically improved the prognosis of patients with metastatic MCC, and are recommended first-line in advanced cases. There is a need for well-tolerated agents with good safety profiles in patients who have failed immunotherapies.