Basic principles of AI simplified for a Medical Practitioner: Pearls and Pitfalls in Evaluating AI algorithms

被引:4
|
作者
Bhalla, Deeksha [1 ]
Ramachandran, Anupama [1 ]
Rangarajan, Krithika [1 ,3 ]
Dhanakshirur, Rohan [2 ]
Banerjee, Subhashis [2 ]
Arora, Chetan [2 ]
机构
[1] All India Inst Med Sci, Dept Radiodiag, Dr BRA IRCH, New Delhi, India
[2] Indian Inst Technol, New Delhi, India
[3] All India Inst Med Sci, Dept Radiodiag, Dr BRAIRCH, New Delhi 110029, India
关键词
ARTIFICIAL-INTELLIGENCE; CROSS-VALIDATION; MACHINE;
D O I
10.1067/j.cpradiol.2022.04.003
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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 (c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页码:47 / 55
页数:9
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