Evidence map›Paper›PMID 42037948›Full record

ArticleCureus2026

When Compliance Is Not Safety: The Regulatory Blind Spot in AI Companion Chatbots.

Esteban Zavaleta-Monestel, Sebastián Arguedas-Chacón, Jeaustin Mora-Jiménez, Ricardo Millán González

Abstract readEditorial
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Esteban Zavaleta-MonestelPharmacy, Hospital Clinica Biblica, San Jose, CRI.
Sebastián Arguedas-ChacónPharmacy, Hospital Clinica Biblica, San Jose, CRI.
Jeaustin Mora-JiménezResearch, Hospital Clínica Bíblica, San José, CRI.
Ricardo Millán GonzálezMedicine, University of Costa Rica, San José, CRI.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current regulations governing artificial intelligence (AI) companion chatbots primarily emphasize disclosure obligations, internal safety protocols, and documentation requirements. AI companion chatbots are systems designed to simulate ongoing social interaction through human-like responses, emotional continuity, and memory across repeated exchanges. However, procedural compliance does not necessarily ensure clinical safety, particularly when adolescents use these systems during emotional crises. Emerging evidence from independent audit studies of consumer chatbots and benchmarking evaluations of large language models (LLMs) for mental health tasks suggests that crisis-response performance can be inconsistent, including variable recognition of suicide risk, inconsistent escalation, limited referral quality, and instability across models and system updates. This editorial argues that safety should be defined by real-world behavioral performance rather than procedural safeguards alone and calls for independent crisis testing, transparent reporting, and longitudinal re-evaluation to better protect vulnerable users.

Indexed as

adolescent mental healthartificial intelligence in psychiatryradiosurgery artificial intelligenceregulatory & compliancesuicide prevention

Identifiers

PMID42037948
PMCPMC13109995

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.