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

Journal: Current problems in diagnostic radiology
Published Date:

Abstract

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.

Authors

  • Deeksha Bhalla
    Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India.
  • Anupama Ramachandran
    Department of Radiodiagnosis, Dr.BRA IRCH, All India Institute of Medical Sciences, New Delhi, India.
  • Krithika Rangarajan
    Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India.
  • Rohan Dhanakshirur
    Indian Institute of Technology, New Delhi, India.
  • Subhashis Banerjee
    Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, India.
  • Chetan Arora
    Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, India.