In Journal of cutaneous pathology
Artificial intelligence utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning artificial intelligence, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, highlighting the potential for deep learning algorithms to improve diagnostic workflow in dermatopathology of highly routine diagnoses. Additionally, convolutional neural networks can support the diagnosis of melanoma and may help predict disease outcomes. Capabilities of machine learning in dermatopathology can extend beyond clinical diagnosis to education and research. Intelligent tutoring systems can teach visual diagnoses in inflammatory dermatoses, with measurable cognitive effects on learners. Natural language interfaces can instruct dermatopathology trainees to produce diagnostic reports that capture relevant detail for diagnosis in compliance with guidelines. Furthermore, deep learning can power computation- and population-based research. However, there are many limitations of deep learning that need to be addressed before broad incorporation into clinical practice. The current potential of artificial intelligence in dermatopathology is to supplement diagnosis, and dermatopathologist guidance is essential for the development of useful deep learning algorithms. Herein, the recent progress of artificial intelligence in dermatopathology is reviewed with emphasis on how deep learning can influence diagnosis, education, and research. This article is protected by copyright. All rights reserved.
Wells Amy, Patel Shaan, Lee Jason B, Motaparthi Kiran
artificial intelligence, convolutional neural network, deep learning, dermatopathology, machine learning