ArticleCureus2026
When Compliance Is Not Safety: The Regulatory Blind Spot in AI Companion Chatbots.
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.
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
1 citing paper in PubMed.
- AI for Clinical Competency Assessment: Scoping Review of Methods and Applications.JMIR medical education · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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.