• Light
  • Dark
  • Auto
Select Page

In this article, the authors aimed to guide and inform the radiology community regarding key methodological aspects of machine learning (ML) in order to improve their academic reading and peer-review experience. This was done so within four broad categories: study design, data handling, modelling, and reporting.

Key points

  • Machine learning is new and rather complex for the radiology community.
  • Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting.
  • Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community.

Article: How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts

Authors: Burak Kocak, Ece Ates Kus & Ozgur Kilickesmez

Latest posts