Smartphone-Assisted Artificial Intelligence in Dermatology- A Novel Approach to Help General Practitioners in Underserved Areas

Recent interest in AI had been driven by an evolution in machine learning resulting in the arrival of ‘deep learning.’ Given sufficient dataset size and processing power, deep learning utilizes Convolutional Neural Networks (CNNs). Deep learning technique is basically the modernized extended version of classical neural networks. The current neural network that is used is more superior in terms of the classical neural network as the current deep learning neural networks had multiple layers [2]. The deep learning method tends to deal with more complex and non-linear data. The deep learning in comparison with the classical neural networks can handle the larger volume and wide complex of data. As it learns directly from the dataset without human direction, deep learning is able to account for inter-data variability as well as process unstandardized data. AI algorithms have been currently used in the diagnosis of diabetic retinopathy, congenital cataracts, melanoma, and onychomycosis [3]. Outside clinical care, AI is being employed to support and potentially replace the roles of healthcare managers in resource, staffing, and financial management.