Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM)
Conclusions
The overall scientific quality of deep learning in knee imaging is insufficient; however, deep learning remains a promising technology for diagnostic or predictive purpose. Improvements in study design, validation, and open science need to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application, pre-trained scoring procedure, and modification of CLAIM in response to clinical needs are necessary in the future.
