Deeksha Bhalla, Anupama Ramachandran, Krithika Rangarajan, Rohan Dhanakshirur, Subhashis Banerjee, Chetan Arora
Curr Probl Diagn Radiol . 2023 Jan-Feb;52(1):47-55. doi: 10.1067/j.cpradiol.2022.04.003. Epub 2022 Apr 22.
With the rapid integration of artificial intelligence into medical practice, there has been an exponential increase in the number of scientific papers and industry players offering models designed for various tasks. Understanding these, however, is difficult for a radiologist in practice, given the core mathematical principles and complicated terminology involved. This review aims to elucidate the core mathematical concepts of both machine learning and deep learning models, explaining the various steps and common terminology in common layman language. Thus, by the end of this article, the reader should be able to understand the basics of how prediction models are built and trained, including challenges faced and how to avoid them. The reader would also be equipped to adequately evaluate various models, and take a decision on whether a model is likely to perform adequately in the real-world setting.