Machine Learning and Artificial Intelligence in Suicide Prevention: A Bibliometric Analysis of Emerging Trends and Implications for Nursing.

Abstract

Nurses play a crucial role in suicide prevention, yet the integration of artificial intelligence and machine learning technologies into nursing practice remains understudied. This research examines how these technologies can enhance nurses' ability to identify and intervene with at-risk patients. A systematic bibliometric analysis and thematic mapping approach was employed. The Web of Science database was searched for relevant publications from January 2019 to October 2024. The initial search yielded 883 publications, with 257 meeting the inclusion criteria after systematic screening. Analysis revealed six distinct research clusters, with machine learning-based behavioral prediction emerging as the dominant theme. Findings indicate significant potential for integrating artificial intelligence-supported tools into nursing workflows, particularly in risk assessment and early intervention. Natural language processing and ecological momentary assessment emerged as promising approaches for enhancing nurse-patient communication and monitoring. These findings suggest opportunities for nurses to leverage artificial intelligence technologies in suicide prevention while maintaining the essential human element of care. This study provides evidence-based guidance for nurses implementing artificial intelligence-supported suicide prevention tools while maintaining therapeutic relationships and professional judgment in clinical practice.