With the booming development of artificial intelligence (AI), machine learning, deep learning and transfer learning have gained growing applications in healthcare, bringing new perspectives and strategies to antidepressant research. Existing antidepressants present slow onset, low efficacy and marked inter-individual variability, with their underlying mechanisms yet to be fully clarified. Traditional research is hampered by small sample sizes, substantial data heterogeneity and poor cross-modal integration capacity. As a pivotal AI technology, transfer learning realizes cross-domain, cross-task and cross-modal knowledge transfer, and mitigates issues such as small-sample modeling, data distribution discrepancy and the curse of dimensionality. This review systematically elaborates the current status and future trends of transfer learning in antidepressant efficacy evaluation and the exploration of their action mechanisms. We analyze the characteristics and performance of transfer learning models construction strategies and application paradigms, and discuss the challenges, technical limitations and clinical translation barriers in practical application. This study provides novel ideas for exploring depression pathogenesis and optimizing clinical antidepressant use, and further promotes the innovative and standardized application of AI in clinical medicine.

transfer learning  /  depression  /  artificial intelligence  /  drug evaluation  /  functional magnetic resonance imaging  /  pharmacogenomics