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This paper is in the following e-collection/theme issue:

Methods and New Tools in Mental Health Research (905) Natural Language Processing (1277) Depression and Mood Disorders; Suicide Prevention (2457) Machine Learning (3167) Generative Language Models Including ChatGPT (1515) Ethical, Legal, and Social Issues in AI (209) Responsible Health AI (224)

Published on in Vol 13 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/88435, first published .
Man thoughtfully looking at AI brain graphic on computer screen

Between Help and Harm: An Evaluation Study of Mental Health Crisis Handling by Large Language Models

Between Help and Harm: An Evaluation Study of Mental Health Crisis Handling by Large Language Models

Authors of this article:

Adrian Arnaiz-Rodriguez1 Author Orcid Image ;   Miguel Baidal1 Author Orcid Image ;   Erik Derner1, 2 Author Orcid Image ;   Jenn Layton Annable3 Author Orcid Image ;   Mark Ball4 Author Orcid Image ;   Mark Ince5 Author Orcid Image ;   Elvira Perez Vallejos6 Author Orcid Image ;   Nuria Oliver1 Author Orcid Image
Article Authors Cited by (2) Tweetations (8) Metrics

Journals

  1. Weilnhammer V, Hou K, Luettgau L, Summerfield C, Dolan R, Nour M. A clinically validated framework for auditing AI chatbot behavior in mental health interactions. Nature Medicine 2026 View
  2. Ball M, Perez Vallejos E. Regulating AI in healthcare: a moving target. BMJ 2026;394:e100555 View