Abstract
In this commentary on 'Translational Machine Learning for Child and Adolescent Psychiatry,' by Dwyer and Koutsouleris, we summarize some of the main points made by the authors, which highlight the importance of emerging applications of machine learning for psychiatric disorders in youth but also emphasize principles of good practice. We also offer complementary insights regarding large-scale training, harmonization, and the ability of these artificial intelligence models to adapt to new datasets, which is critical for their stability across imaging centers, and hence for their widespread clinical adoption.