This paper is in the following e-collection/theme issue:

Generative Language Models Including ChatGPT (725) Depression and Mood Disorders; Suicide Prevention (1703) Digital Health Reviews (1742) Methods and New Tools in Mental Health Research (562) Intentional Self-Harm (138) Digital Health, Telehealth and e-Innovation in Clinical Settings (561) Ethics, Privacy, and Legal Issues (622) mHealth for Wellness, Behavior Change and Prevention (3339)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63126, first published .
Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review

Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review

Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review

Authors of this article:

Glenn Holmes1 Author Orcid Image ;   Biya Tang1 Author Orcid Image ;   Sunil Gupta2 Author Orcid Image ;   Svetha Venkatesh2 Author Orcid Image ;   Helen Christensen1 Author Orcid Image ;   Alexis Whitton1 Author Orcid Image
Article Authors Cited by (10) Tweetations (1) Metrics

Journals

  1. Ni Y, Jia F. A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education. Healthcare 2025;13(10):1205 View
  2. Greaves J, Colucci E. Crisis-line workers’ perspectives on AI in suicide prevention: a qualitative exploration of risk and opportunity. BMC Public Health 2025;25(1) View
  3. Sherekar P, Mehta M. Harnessing technology for hope: a systematic review of digital suicide prevention tools. Discover Mental Health 2025;5(1) View
  4. Nadkarni A, Kroll D, Silbersweig D. After Patient Suicide: Fostering a Culture of Patient Safety and Clinician Well-Being to Improve Quality of Care. Harvard Review of Psychiatry 2025;33(4):239 View
  5. Cui X, Gu Y, Fang H, Zhu T. Development and evaluation of LLM-based suicide intervention chatbot. Frontiers in Psychiatry 2025;16 View
  6. da Silva R, Pazin-Filho A. The incremental value of unstructured data via natural language processing in machine learning-based COVID-19 mortality prediction: a comparative study. BMC Medical Informatics and Decision Making 2025;25(1) View
  7. Elyospeh Z, Nobile B, Levkovich I, Chancel R, Courtet P, Levi-Belz Y. The role of generative artificial intelligence in evaluating adherence to responsible press media reports on suicide: A multisite, three-language study. European Psychiatry 2025;68(1) View
  8. Liu L, Franklin R, Cheong J, Cong T, Byun J, Oh A, Torous J. H3-MOSAIC: multimodal generative AI for semantic place detection from high-frequency GPS on H3 grids in mental health geomatics. International Journal of Health Geographics 2025;24(1) View

Conference Proceedings

  1. Azevedo J, de Oliveira A, Silva F, Coutinho L, Teles A. 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). Harnessing Generative Llms to Detect and Explain Suicidal Ideation in Brazilian Portuguese Texts View
  2. Amine I, Lmati I, Anoun H, Ammor F. 2025 International Conference on Intelligent Systems: Theories and Applications (SITA). Use, Performance, and Limitations of Large Language Models in Mental Health: A Systematic Review View