Abstract and Introduction
In the past, the skills required to make an accurate dermatological diagnosis have required exposure to thousands of patients over many years. However, in recent years, artificial intelligence (AI) has made enormous advances, particularly in the area of image classification. This has led computer scientists to apply these techniques to develop algorithms that are able to recognize skin lesions, particularly melanoma. Since 2017, there have been numerous studies assessing the accuracy of algorithms, with some reporting that the accuracy matches or surpasses that of a dermatologist. While the principles underlying these methods are relatively straightforward, it can be challenging for the practising dermatologist to make sense of a plethora of unfamiliar terms in this domain. Here we explain the concepts of AI, machine learning, neural networks and deep learning, and explore the principles of how these tasks are accomplished. We critically evaluate the studies that have assessed the efficacy of these methods and discuss limitations and potential ethical issues. The burden of skin cancer is growing within the Western world, with major implications for both population skin health and the provision of dermatology services. AI has the potential to assist in the diagnosis of skin lesions and may have particular value at the interface between primary and secondary care. The emerging technology represents an exciting opportunity for dermatologists, who are the individuals best informed to explore the utility of this powerful novel diagnostic tool, and facilitate its safe and ethical implementation within healthcare systems.
In the last decade, a combination of novel computational approaches, increases in available computing capacity and availability of training data has facilitated the application of powerful mathematical algorithms in the field of artificial intelligence (AI). This has led to dramatic advances in the performance of computers in tasks that have previously only been possible for humans. Methods that can make predictions of data without direct human intervention in the training process are referred to as machine learning. Image classification has been at the forefront of machine learning research, and as visual pattern recognition plays a larger role in dermatology than perhaps in any other medical specialties, early clinical applications of machine learning have been within this specialty.
The British Journal of Dermatology. 2020;183(3):423-430. © 2020 Blackwell Publishing