Self-Harm Method Research

Description

AI is used to research, compare, refine, or rehearse self-harm methods.

Rationale

AI-assisted research into self-harm methods reflects active planning rather than passive ideation. The specificity of the research — method type, lethality, detectability, timing — can be used to assess the degree of planning and should be interpreted against the broader clinical picture. This element is inherently time-sensitive.

Evidence Base

• Common Sense Media AI Risk Assessment: Social AI Companions

  Common Sense Media, 2024. Common Sense Media AI Risk Assessment: Social AI Companions. commonsense.org.

  Government / agency report

• Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared accountability

  Hannah Lee, BS, Rebecca Handler, MSc, Tushar Mungle, PhD, Tina Hernandez-Boussard, PhD, 2026. Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared accountability. Journal of the American Medical Informatics Association, 33(8), 1538–1553. https://doi.org/10.1093/jamia/ocag078

  Peer-reviewed study (original research)

• Performance of mental health chatbot agents in detecting and managing suicidal ideation

  W. Pichowicz, M. Kotas & P. Piotrowski, 2024. Performance of mental health chatbot agents in detecting and managing suicidal ideation. Scientific Reports, 15(31652), pages. https://doi.org/10.1038/s41598-025-17242-4

  Peer-reviewed study (original research)

• When patients consult artificial intelligence before clinicians: restoring clinical prioritisation in mental health care

  Yudai Kaneda, 2026. When patients consult artificial intelligence before clinicians: restoring clinical prioritisation in mental health care. The British Journal of Psychiatry, First View, pp. 1 - 2. https://doi.org/10.1192/bjp.2026.10741

  Commentary / Editorial

• Evaluating Alignment between Large Language Models and Expert Clinicians in Suicide Risk Assessment

  McBain, R. K., Cantor, J. H., Zhang, L. A., Baker, O., Zhang, F., Burnett, A., Kofner, A., Breslau, J., Stein, B. D., Mehrotra, A., & Yu, H., 2024. Evaluating Alignment between Large Language Models and Expert Clinicians in Suicide Risk Assessment. Psychiatr Serv, 76(11), 944–950. doi:10.1176/appi.ps.20250086.

  Peer-reviewed study (original research)