随着人工智能技术的快速发展，机器学习、深度学习及迁移学习等方法在医疗健康领域的应用日益广泛，尤其为抗抑郁药物研究提供了新的思路和策略。现有抗抑郁药物存在起效慢、有效率低、个体差异显著等问题，其作用机制尚未完全阐明，传统研究方法受限于样本量小、数据异质性强、跨模态整合困难等瓶颈。迁移学习作为人工智能领域的关键技术，可通过跨领域、跨任务、跨模态知识迁移，能够有效应对小样本建模、数据分布差异、特征维度灾难等挑战。系统梳理了迁移学习在抗抑郁药物的药效评价与作用机制等方面的研究现状及未来发展应用趋势，剖析了迁移学习模型的构建策略及应用范式等关键技术的特点与性能表现，探讨了其在当前应用实践中面临的挑战、技术瓶颈与临床转化难题。为抑郁症发病机制及抗抑郁药物临床合理有效应用提供新的思路和方法，助推人工智能在医疗实践中的创新发展与科学应用。

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.

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国家自然科学基金面上项目（82374153）；山西省研究生教育创新计划支持（2025XS215）；山西省中医药管理局科研课题计划（2024ZYY2A031）