The application of artificial intelligence in insomnia, anxiety, and depression: A bibliometric analysis

Abstract

Background: Mental health issues like insomnia, anxiety, and depression have increased significantly. Artificial intelligence (AI) has shown promise in diagnosing and providing personalized treatment. Objective: This study aims to systematically review the application of AI in addressing insomnia, anxiety, and depression, identifying key research hotspots, and forecasting future trends through bibliometric analysis. Methods: We analyzed a total of 875 articles from the Web of Science Core Collection (2000-2024) using bibliometric tools such as VOSviewer and CiteSpace. These tools were used to map research trends, highlight international collaboration, and examine the contributions of leading countries, institutions, and authors in the field. Results: The United States and China lead the field in terms of research output and collaborations. Key research areas include "neural networks," "machine learning," "deep learning," and "human-robot interaction," particularly in relation to personalized treatment approaches. However, challenges around data privacy, ethical concerns, and the interpretability of AI models need to be addressed. Conclusions: This study highlights the growing role of AI in mental health research and identifies future priorities, such as improving data quality, addressing ethical challenges, and integrating AI more seamlessly into clinical practice. These advancements will be crucial in addressing the global mental health crisis.