Evidence map›Paper›PMID 42466259›Full record

ReviewGlobal mental health (Cambridge, England)2026

Are artificial intelligence chatbots safe for suicide risk assessment? A narratively synthesized review of current evidence.

Khaled Elbarbary, Amani Alfudoul, Ahmed Amir Samir, Husam Aldean H Hussain, Balgees Altayib, Amjed Marzouq Abdelqader Abdel Al, George Jabrieh, Rahaf Shaban, Sheikh Shoib, Abdel-Hady El-Gilany

Abstract readReview
In one paragraph

Review in Global mental health (Cambridge, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Khaled ElbarbaryMansoura University Faculty of Medicine, Egypt.ORCID https://orcid.org/0009-0009-5237-3320
Amani AlfudoulMedical Biotechnology and Translational Medicine, University of Milan, Milan, Jordan.
Ahmed Amir SamirAl-Azhar University Faculty of Medicine, Egypt.
Husam Aldean H HussainJordan University of Science and Technology, Jordan.
Balgees AltayibUniversity of Bahri, Sudan.
Amjed Marzouq Abdelqader Abdel AlJordan University of Science and Technology, Jordan.
George JabriehPalestine Polytechnic University, Palestinian Territory, Occupied.
Rahaf ShabanYeni Yuzyil University, Türkiye.
Sheikh ShoibDepartment of Health Services, Srinagar, Kashmir, India.
Abdel-Hady El-GilanyMansoura University Faculty of Medicine, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Suicide is a significant global public health concern, and conventional clinical risk assessment is constrained by workforce availability, scalability, and clinician variability. The classic suicidality risk evaluation is largely dependent on clinical judgment, which, although helpful, can demonstrate a ceiling effect in its predictive validity. AI-driven chatbots, conversational systems that engage users in real-time natural language dialog, have emerged as candidate tools for augmenting suicide risk detection and prevention. This narrative review aimed to: evaluate the performance of AI-driven chatbots in detecting and assessing suicidal ideation relative to clinical benchmarks; examine the effectiveness of chatbot-based interventions for suicide prevention; and identify ethical, cultural, and implementation challenges limiting clinical translation. Six electronic databases were searched, with the initial search conducted in 2024 and updated in 2026 through targeted monitoring, with no upper cutoff date applied. A thematic narrative synthesis approach was applied across five domains. Eleven primary studies met eligibility criteria and were included in the synthesis. Chatbot-based risk assessment showed adequate response alignment with expert judgment at the extremes of suicide risk, but consistently failed to distinguish intermediate risk levels across multiple model families. Across 29 tested commercial chatbot agents, none met the criteria for an adequate suicidal crisis response. A clinically designed, framework-anchored chatbot achieved high efficacy across six outcome domains. Three percent of social chatbot users reported halted suicidal ideation, and a purpose-built clinical chatbot in emergency department settings significantly improved evidence-based care delivery. Systematic risk severity underestimation and the absence of cross-cultural evaluation were identified across all studies. AI-driven chatbots show potential as adjunctive tools across the suicide care continuum. Clinically designed, evidence-anchored chatbots demonstrate feasibility and meaningful benefit; commercially deployed chatbots without clinical validation demonstrate near-universal crisis response inadequacy. Mandatory clinical validation prior to public release, clinician oversight, and crisis system integration are prerequisites for responsible deployment.

Indexed as

artificial intelligencechatbotsmental health servicesnatural language processingsuicidal ideation

Identifiers

PMID42466259
PMCPMC13373273

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.