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.
Authors: Burak Kocak, Ece Ates Kus & Ozgur Kilickesmez
