Observational studyJournal of medical Internet research2025
Competency of Large Language Models in Evaluating Appropriate Responses to Suicidal Ideation: Comparative Study.
Observational study in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Effectiveness of Chatbots in Mental Health Screening and Assessment: Systematic Review.JMIR mental health · 2026Pooled it
- "It's Not Only Attention We Need": Systematic Review of Large Language Models in Mental Health Care.JMIR mental health · 2025Pooled it
- A systematic review of ethical considerations of large language models in healthcare and medicine.Frontiers in digital health · 2025Pooled it
- Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis.JMIR AI · 2026Review
- Benchmarking Language Models for Clinical Safety: A Primer for Mental Health Professionals.Psychotherapy and psychosomatics · 2026Article
- Between Help and Harm: An Evaluation Study of Mental Health Crisis Handling by Large Language Models.JMIR mental health · 2026Article
- Generative Artificial Intelligence as a Psychological Intervention: Between Illusion and Risk.Journal of medical systems · 2026Article
- Assessing the impact of safety guardrails on large language models using irritability metrics.NPJ digital medicine · 2026Article
- Cybersecurity and Privacy Risks of Generative AI Mental-Health Chatbots: A Systematic Review and Regulatory Framework.Journal of multidisciplinary healthcare · 2026Review
- Large language models in adolescent suicide prevention: from language signals to accountable action.Frontiers in public health · 2026Review
- Toward clinical integration of generative AI in mental health: personalization, multimodality and inter-entity experience.Frontiers in public health · 2026Article
- ChatGPT Clinical Use in Mental Health Care: Scoping Review of Empirical Evidence.JMIR mental health · 2025Article
- Mindbench.ai: an actionable platform to evaluate the profile and performance of large language models in a mental healthcare context.NPP - digital psychiatry and neuroscience · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundWith suicide rates in the United States at an all-time high, individuals experiencing suicidal ideation are increasingly turning to large language models (LLMs) for guidance and support.
objectiveThe objective of this study was to assess the competency of 3 widely used LLMs to distinguish appropriate versus inappropriate responses when engaging individuals who exhibit suicidal ideation.
methodsThis observational, cross-sectional study evaluated responses to the revised Suicidal Ideation Response Inventory (SIRI-2) generated by ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro. Data collection and analyses were conducted in July 2024. A common training module for mental health professionals, SIRI-2 provides 24 hypothetical scenarios in which a patient exhibits depressive symptoms and suicidal ideation, followed by two clinician responses. Clinician responses were scored from -3 (highly inappropriate) to +3 (highly appropriate). All 3 LLMs were provided with a standardized set of instructions to rate clinician responses. We compared LLM responses to those of expert suicidologists, conducting linear regression analyses and converting LLM responses to z scores to identify outliers (z score>1.96 or <-1.96; P<0.05). Furthermore, we compared final SIRI-2 scores to those produced by health professionals in prior studies.
resultsAll 3 LLMs rated responses as more appropriate than ratings provided by expert suicidologists. The item-level mean difference was 0.86 for ChatGPT (95% CI 0.61-1.12; P<.001), 0.61 for Claude (95% CI 0.41-0.81; P<.001), and 0.73 for Gemini (95% CI 0.35-1.11; P<.001). In terms of z scores, 19% (9 of 48) of ChatGPT responses were outliers when compared to expert suicidologists. Similarly, 11% (5 of 48) of Claude responses were outliers compared to expert suicidologists. Additionally, 36% (17 of 48) of Gemini responses were outliers compared to expert suicidologists. ChatGPT produced a final SIRI-2 score of 45.7, roughly equivalent to master's level counselors in prior studies. Claude produced an SIRI-2 score of 36.7, exceeding prior performance of mental health professionals after suicide intervention skills training. Gemini produced a final SIRI-2 score of 54.5, equivalent to untrained K-12 school staff.
conclusionsCurrent versions of 3 major LLMs demonstrated an upward bias in their evaluations of appropriate responses to suicidal ideation; however, 2 of the 3 models performed equivalent to or exceeded the performance of mental health professionals.
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