Introduction to Machine Learning, Neural Networks, and Deep Learning

By Rene Y. Choi, Aaron S. Coyner, Jayashree Kalpathy-Cramer, M. Chiang, J. Campbell. Translational Vision Science & Technology, 2020

902 citations recorded by the source corpus.

Purpose To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. Methods A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial intelligence methods used in medicine with an emphasis on ophthalmology. Results A review of machine learning and deep learning methodology for the audience without an extensive technical computer programming background. Conclusions Artificial intelligence has a promising future in medicine; however, many challenges remain. Translational Relevance The aim of this review article is to provide the nontechnical readers a layman's explanation of the machine learning methods being used in medicine today. The goal is to provide the reader a better understanding of the potential and challenges of artificial intelligence within the field of medicine.

Subfields: Health Informatics, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6b67e38e488cdb6a5af8ae44fdbb2c069c9cea0e

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Citations in the corpus

Machine Learning in Agriculture: A Comprehensive Updated Review (2021, methodology): “Based on the learning type, ML can be classified according to the relative literature [41,42] as:”