Artificial Intelligence in Psychiatry: A Clinical Framework for the Supervised Integration of Large Language Models when Patients Are Already Using Them

Abstract

The use of large language models (LLMs) by patients with psychiatric conditions is a present and irreversible clinical reality. Patients increasingly arrive at consultation having already employed artificial intelligence (AI) systems as informal mental health advisors to interpret symptoms, make decisions, and regulate emotions. This unsupervised use carries documented risks: reinforcement of cognitive distortions, generation of clinically incorrect information through model hallucination, and inadequate crisis management. In a recent 18-month ethnographic study, licensed clinical psychologists documented 15 ethical violations produced by LLMs that were “consulted” as cognitive behavioral therapy (CBT) counselors. Yet randomized controlled trials demonstrate that structured, CBT-aligned conversational agents, when properly bounded and supervised, produce clinically significant reductions in anxiety and depressive symptoms as adjuncts to formal care. This review proposes a five-step clinical framework, operationalizable within the standard psychiatric encounter, to transform unguided AI use into a structured, supervised therapeutic tool. The framework delineates clinically appropriate versus inappropriate uses of AI, defines absolute and relative contraindications, and integrates ethical considerations on transparency, accountability, and patient privacy. If patients are going to use AI regardless, it is the clinician’s professional responsibility to teach them how to do so safely within the therapeutic frame.