Evidence map›Paper›PMID 40053817›Full record

Observational studyJournal of medical Internet research2025

Competency of Large Language Models in Evaluating Appropriate Responses to Suicidal Ideation: Comparative Study.

Ryan K McBain, Jonathan H Cantor, Li Ang Zhang, Olesya Baker, Fang Zhang, Alyssa Halbisen, Aaron Kofner, Joshua Breslau, Bradley Stein, Ateev Mehrotra and 1 more

Abstract readComparative StudyObservational Study
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ryan K McBainRAND, Arlington, VA, United States.ORCID https://orcid.org/0000-0003-0073-0348
Jonathan H CantorRAND, Santa Monica, CA, United States.ORCID https://orcid.org/0000-0003-4468-833X
Li Ang ZhangRAND, Santa Monica, CA, United States.ORCID https://orcid.org/0000-0001-9468-2513
Olesya BakerHarvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0001-9125-2761
Fang ZhangHarvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0002-8282-8738
Alyssa HalbisenHarvard Pilgrim Health Care Institute, Boston, MA, United States.ORCID https://orcid.org/0009-0000-0482-729X
Aaron KofnerRAND, Arlington, VA, United States.ORCID https://orcid.org/0000-0001-6980-1218
Joshua BreslauRAND, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0002-1194-4643
Bradley SteinRAND, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0003-1544-458X
Ateev MehrotraBrown University School of Public Health, Providence, RI, United States.ORCID https://orcid.org/0000-0003-2223-1582
Hao YuHarvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0001-6169-4243

Funding

State Telehealth Policies and Mental Care for Children in Underserved AreasR01MH132551 · NIMH · HARVARD PILGRIM HEALTH CARE, INC. · PI HAO YU · 2024 to 2026
$2.3M
NIMH NIH HHS R01 MH132551
6 · The paper itself

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.

Indexed as

Suicidal IdeationAdultCross-Sectional StudiesFemaleHumansLarge Language ModelsMaleartificial intelligencechatbotChatGPTdepressiondigital healthlarge language modelmental healthSuicidal Ideation Response Inventorysuicidesuicidologist

Identifiers

PMID40053817
PMCPMC11928068

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.