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The rapid diffusion of generative artificial intelligence chatbots has repositioned these systems from productivity tools to interlocutors used for emotional support, companionship, and mental health guidance. This critical narrative review has three aims: to give mental health professionals a concise technical account of large language models; to separate the evidence and risks attached to four distinct system classes, protocolized therapeutic chatbots, clinically bounded generative systems, general-purpose large language models used as informal therapy, and companion platforms; and to examine clinical, ethical, and regulatory implications. Literature was identified through non-systematic, iterative database and preprint-server searches between March and July 2026, supplemented by citation tracking and policy documents. No prespecified eligibility or screening protocol was used; the comparative claims are therefore provisional. Protocolized and clinically bounded systems carry the strongest evidence, including a meta-analysis of generative and hybrid chatbots reporting modest short-term symptom reduction and a randomized trial of an expert-fine-tuned therapeutic chatbot; these trials are brief, predominantly waitlist-controlled, and reliant on self-report. General-purpose models and companion platforms may deliver immediate validation and temporary reductions in loneliness, but that evidence is largely observational, qualitative, or short-term experimental and does not establish sustained benefit. Concerns include emotional dependence, social substitution, sycophantic reinforcement, unsafe responses to suicidal or psychotic content, commercial manipulation, and delusional beliefs temporally associated with sustained use. These systems are not a single intervention class. Their effects appear contingent on clinical structure, relational design, user vulnerability, duration and centrality of use, commercial incentives, and safeguards.
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Santos et al. (2026) studied this question.
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