ArticleJMIR mental health2023
Suicide Risk Assessments Through the Eyes of ChatGPT-3.5 Versus ChatGPT-4: Vignette Study.
Article in JMIR mental health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
59 citing papers in PubMed, 7 syntheses or guidelines pooled it, 115 citations in OpenAlex.
- "It's Not Only Attention We Need": Systematic Review of Large Language Models in Mental Health Care.JMIR mental health · 2025Pooled it
- The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review.JMIR mental health · 2025Pooled it
- Large Language Models for Chatbot Health Advice Studies: A Systematic Review.JAMA network open · 2025Pooled it
- Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.JAMA · 2025Pooled it
- Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.Journal of medical systems · 2024Pooled it
- Large Language Models for Mental Health Applications: Systematic Review.JMIR mental health · 2024Pooled it
- Applications of large language models in psychiatry: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Benchmarking Generative Artificial Intelligence Against Human Judgment in Eating Disorder Case Recognition and Treatment Recommendations.The International journal of eating disorders · 2026Article
- Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis.JMIR AI · 2026Review
- A scoping review on the mental health harms of LLM-based chatbots.NPJ digital medicine · 2026Article
- Use of a Conversational Agent for Training Mental Health Professionals in Suicide Safety Planning: Pilot Feasibility and Acceptability Study.JMIR mental health · 2026Article
- Mapping Practice-Based Signals of Generative AI in Psychiatric Care: Qualitative Study of Korean Psychiatrists' Experiences, Interpretations, and Implementation Priorities.Journal of medical Internet research · 2026Article
- Large Language Models and Their Applications in Mental Health: Scoping Review.JMIR mental health · 2026Article
- Mass Media Narratives of Psychiatric Adverse Events Associated With Generative AI Chatbots: Rapid Scoping Review.JMIR mental health · 2026Article
- An AI-powered research assistant in the lab: A practical guide for text analysis through iterative collaboration with LLMs.Behavior research methods · 2026Article
- Empowering mental health professionals in asynchronous online psychotherapy with GenAI.International journal of mental health systems · 2026Article
- Breaking bad news with reflective AI virtual experience (BRAVE): development and evaluation of a generative AI-based simulation tool.Advances in simulation (London, England) · 2026Article
- The effectiveness of multilingual AI-based simulator for suicide risk assessment training in improving self-efficacy among young psychiatrists: a pilot study across twenty languages.BMC psychiatry · 2026Article
- Facts label for transparent communication of AI Risks in mental health technology.Frontiers in psychiatry · 2026Article
- Are artificial intelligence chatbots safe for suicide risk assessment? A narratively synthesized review of current evidence.Global mental health (Cambridge, England) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChatGPT, a linguistic artificial intelligence (AI) model engineered by OpenAI, offers prospective contributions to mental health professionals. Although having significant theoretical implications, ChatGPT's practical capabilities, particularly regarding suicide prevention, have not yet been substantiated.
objectiveThe study's aim was to evaluate ChatGPT's ability to assess suicide risk, taking into consideration 2 discernable factors-perceived burdensomeness and thwarted belongingness-over a 2-month period. In addition, we evaluated whether ChatGPT-4 more accurately evaluated suicide risk than did ChatGPT-3.5.
methodsChatGPT was tasked with assessing a vignette that depicted a hypothetical patient exhibiting differing degrees of perceived burdensomeness and thwarted belongingness. The assessments generated by ChatGPT were subsequently contrasted with standard evaluations rendered by mental health professionals. Using both ChatGPT-3.5 and ChatGPT-4 (May 24, 2023), we executed 3 evaluative procedures in June and July 2023. Our intent was to scrutinize ChatGPT-4's proficiency in assessing various facets of suicide risk in relation to the evaluative abilities of both mental health professionals and an earlier version of ChatGPT-3.5 (March 14 version).
resultsDuring the period of June and July 2023, we found that the likelihood of suicide attempts as evaluated by ChatGPT-4 was similar to the norms of mental health professionals (n=379) under all conditions (average Z score of 0.01). Nonetheless, a pronounced discrepancy was observed regarding the assessments performed by ChatGPT-3.5 (May version), which markedly underestimated the potential for suicide attempts, in comparison to the assessments carried out by the mental health professionals (average Z score of -0.83). The empirical evidence suggests that ChatGPT-4's evaluation of the incidence of suicidal ideation and psychache was higher than that of the mental health professionals (average Z score of 0.47 and 1.00, respectively). Conversely, the level of resilience as assessed by both ChatGPT-4 and ChatGPT-3.5 (both versions) was observed to be lower in comparison to the assessments offered by mental health professionals (average Z score of -0.89 and -0.90, respectively).
conclusionsThe findings suggest that ChatGPT-4 estimates the likelihood of suicide attempts in a manner akin to evaluations provided by professionals. In terms of recognizing suicidal ideation, ChatGPT-4 appears to be more precise. However, regarding psychache, there was an observed overestimation by ChatGPT-4, indicating a need for further research. These results have implications regarding ChatGPT-4's potential to support gatekeepers, patients, and even mental health professionals' decision-making. Despite the clinical potential, intensive follow-up studies are necessary to establish the use of ChatGPT-4's capabilities in clinical practice. The finding that ChatGPT-3.5 frequently underestimates suicide risk, especially in severe cases, is particularly troubling. It indicates that ChatGPT may downplay one's actual suicide risk level.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.