AI Applications in Depression Detection and Diagnosis: Bibliometric and Visual Analysis of Trends and Future Directions.

Background: Depression is a highly prevalent and debilitating mental disorder, but its diagnosis largely relies on subjective assessments, creating challenges in accuracy and consistency. Advances in artificial intelligence (AI) offer promising avenues for more objective and efficient diagnostic approaches. Understanding the evolving landscape of AI applications in depression diagnosis is essential for guiding future research and clinical translation.

Objective: This study aims to provide a comprehensive bibliometric and visual analysis of the global research trends, intellectual structure, and emerging frontiers in the application of AI for depression detection and diagnosis from 2015 to 2024.

Methods: We conducted a systematic literature search in the Web of Science Core Collection database to identify publications on AI applications in depression diagnosis from January 1, 2015, to December 31, 2024. A total of 2304 articles were retrieved and analyzed using bibliometric software CiteSpace. The analysis encompassed temporal trends, keyword dynamics, author collaboration networks, institutional influence, country contributions, and intellectual foundations through co-citation analysis of journals and references.

Results: The field exhibited exponential growth in publications and citations, particularly after 2018, reflecting increasing academic and clinical interest. Key thematic shifts were observed from traditional machine learning to advanced deep learning, multimodal fusion, and the integration of objective biomarkers (eg, electroencephalography, facial expressions). Leading contributors included institutions from China and the United States, with collaborative links also forming with countries such as Canada and Singapore. The intellectual base is highly interdisciplinary, drawing heavily from computer science, neuroscience, and psychiatry, with a notable surge in engineering and translational research.

Conclusions: The integration of AI in depression diagnosis is a rapidly maturing and diversifying field, transitioning from theoretical exploration to clinically relevant applications that emphasize objective, data-driven approaches. The identified trends underscore the need for enhanced interdisciplinary and international collaboration, the development of ethical frameworks, and a focus on translating technological innovations into accessible and equitable mental health solutions. These findings offer valuable insights for researchers, clinicians, and policy makers to strategically advance AI-assisted depression diagnostics globally.