The mechanism and research progress of negative visual attention bias in the diagnosis and treatment of depression

Negative visual attention bias, a core pathological feature of depression, is crucial for its diagnosis and treatment. Related research has become a cutting-edge focus in the cognitive neuroscience of depression. This article focuses on the diagnostic and therapeutic value of negative visual attention bias as a key pathological target in depression, systematically reviewing the paradigm shifts, key advances, and current challenges in this research field driven by artificial intelligence (AI) over the past three years. First, we outline the basic concepts of negative visual attention bias and its central role in the pathogenesis of depression. Next, we focus on three breakthrough directions driven by AI: dynamic modeling of neural mechanisms, multimodal AI assessment techniques, and innovative AI-assisted precision intervention strategies. We also provide an in-depth analysis of the technical limitations and standardization challenges currently facing AI-driven research. Finally, we explore future developments, such as the development of closed-loop precision diagnosis and treatment systems integrating multidimensional data from dynamic brain networks, multi-omics, and digital phenotyping. This review of existing research aims to provide both theoretically insightful and technologically cutting-edge insights for deepening our understanding of the neural mechanisms of negative visual attention bias and promoting the clinical translation of precision diagnosis and treatment for depression.