Every Mental Health Practice Needs a Position on Clients’ Use of AI
AI has already entered care through the client, whether your practice uses it or not.
Mental health practices can no longer remain neutral on client AI use. By the time a client reaches a clinician, AI may already have shaped the story they tell about themselves.
Clients arrive with explanations of their symptoms, expectations about diagnosis, interpretations of relationships and techniques they have already tried that may all have been influenced by AI. They may also have disclosed sensitive information to systems outside the protections of clinical care. All of this changes what clinicians need to ask, assess and address.
Consider a composite case. A client becomes convinced that her partner is having an affair. Between sessions, she pastes his texts into a general-purpose chatbot and asks what they might mean. The chatbot cannot know his intentions, but it can generate plausible interpretations of ambiguous wording. As she returns with more excerpts and follow-up questions, those interpretations accumulate into a coherent account of her suspicion. By the next appointment, she is presenting a case the chatbot has helped her assemble.
How should you respond? What should count as evidence, when should you review the transcript, and how do you decide whether the interaction has become clinically significant?
This essay proposes a practical framework for answering those questions, responding proportionately, protecting privacy and recognising when awareness becomes professional involvement.
Client AI Use Is Now Part of Clinical Practice
AI use now belongs within ordinary clinical assessment because it is already part of what many clients bring into treatment. In the American Psychological Association’s 2026 Chatbots and Mental Health Survey
77% discussed AI use with patients
39% patients used AI to self-diagnose
35% patients used AI as an additional mental health professional
33% patients used AI to assist treatment
13% patients engaged with chatbots in intimate relationships
Much of that use is understandable. AI is available when therapy is not, and it can help someone find language for an experience, organise questions or practise a skill. The same accessibility can also support repeated reassurance-seeking, extensive disclosure and unwarranted confidence in an answer that sounds more certain than the evidence warrants.
Clinically, these systems are not interchangeable. A general-purpose chatbot, an AI companion designed to sustain ongoing interaction and a mental health application built around a defined intervention are different products with different evidence, incentives and risk profiles. The APA’s health advisory on chatbots and wellness applications draws similar distinctions and cautions against applying evidence from purpose-built interventions to general-purpose systems.
Clinicians therefore need judgement rather than a default stance for or against AI. Reflexive dismissal can discourage disclosure of use that may already be influencing the client. Uncritical acceptance can allow unsupported conclusions to enter assessment, formulation and treatment.
Why Conversational AI Is Different From “Dr. Google”
A chatbot does something a search engine never did. It responds to the client’s framing, mirrors their language and generates a new interpretation with each follow-up. The exchange can feel less like reading information and more like being understood. The APA’s guide to navigating AI-generated advice warns that mirroring can create a sense of being known and that confident language can make inaccurate information seem credible.
That influence can accumulate across a conversation. Each response may build on the last, organise ambiguous events into a coherent account and increase confidence without adding independent evidence. Repeated, personalised interaction can therefore begin to shape how a client understands symptoms, relationships or themselves, rather than simply supplying information.
How to Judge the Clinical Significance of Client AI Use
AI use becomes clinically relevant when it begins to influence what a client believes, feels or does. The same tool can play a very different role depending on the person, the purpose and the pattern of use.
One client may use a chatbot to prepare questions for a clinician. Another may return repeatedly for reassurance about a feared illness. A client with obsessive-compulsive symptoms may use it for checking, while someone developing paranoid beliefs may ask it to interpret ambiguous events.
These uses do not warrant the same response. Client AI use becomes clinically significant when it meaningfully affects symptoms, beliefs, behaviour, relationships, functioning, treatment participation, privacy or risk.
That standard allows clinicians to ask without treating every use as a problem or every conversation as something they may inspect. Clinicians already explore outside influences when they affect presentation, maintain symptoms or alter risk. AI requires the same judgement, with added attention to repeated interaction and the authority clients may attribute to its responses.
The clinical response should match the level of concern. I propose three levels.
Level one involves acknowledgement and brief guidance. The client may use AI to prepare questions, organise thoughts or rehearse a conversation without evidence that the interaction is reinforcing symptoms or interfering with care. The clinician can discuss accuracy, privacy and the limits of AI-generated advice, then revisit the issue if its role changes.
Level two requires active clinical work. AI has become part of reassurance-seeking, rumination, compulsive checking, avoidance, rigid certainty, relationship conflict or disengagement from treatment. The clinician should examine what function the interaction serves, how often it occurs, what happens afterwards and whether the client is beginning to rely on the system’s interpretation over other sources of evidence. Clinical work may include separating direct experience from AI-generated interpretation and addressing the behaviour within the existing formulation and treatment plan.
Level three requires direct risk management. AI use is occurring alongside acute suicidality, emerging psychosis or mania, severe impairment in judgement, threats or violence, exploitation, inability to maintain safety or other circumstances in which the interaction may be amplifying immediate risk. The clinician should assess the underlying clinical state directly rather than treating the chatbot exchange as evidence in itself, and follow established crisis, psychiatric, safeguarding or emergency procedures as indicated.
These levels are dynamic. A client can move between them as the function and consequences of AI use change.
The composite client introduced at the beginning of this essay, who brought suspicious text messages to therapy after discussing them with AI, would initially fall at level two. Escalating surveillance, confrontation, sleep loss, increasing conviction despite contrary evidence or broader suspiciousness would raise concern about movement towards level three. A practice position should help the clinician recognise that trajectory before a crisis determines the response.
How to Assess AI-Generated Diagnoses and Formulations
Once AI use begins to influence a client’s beliefs, behaviour or treatment, the clinician needs to examine what the system has actually contributed. The central task is to separate the client’s experience from the interpretation the chatbot has built around it.
The problem is that generative AI can turn incomplete or ambiguous information into a coherent psychological account. In the JAMA Psychiatry Viewpoint “LLMs as Clinical Instruments—Toward Verifiable Reasoning”, Martin Paulus and John Torous describe how LLMs can omit relevant details, introduce unsupported content and smooth contradictions into a plausible narrative. They recommend separating facts from inferences, identifying what information is missing, challenging the initial answer and grounding factual claims in source material.
Paulus and Torous developed that discipline for clinicians using LLMs within clinical workflows, but the same logic applies when a client brings an AI-generated diagnosis, formulation or interpretation into treatment. The output may be worth exploring as part of the clinical material. It does not become collateral evidence, a psychological assessment or an established diagnosis simply because it is detailed, confident or psychologically sophisticated.
The clinician should reconstruct how the conclusion was produced.
What did the client tell the system?
How did they frame the question?
Did they begin by suggesting a diagnosis or explanation?
What relevant history, contradictory information or contextual detail never entered the conversation?
What did the output change?
Did it help the client describe an experience more clearly?
Did it narrow attention around one explanation?
Did it increase certainty before an adequate assessment?
The composite client from the opening illustrates the problem. Her partner’s delayed replies and ambiguous wording are observations. The conclusion that those messages indicate an affair is an interpretation. Repeated chatbot exchanges can elaborate that interpretation, organise additional details around it and make the resulting account feel progressively more convincing without adding independent evidence.
The same clinical discipline still applies. Establish what happened, distinguish observation from inference, consider competing explanations and assess the client’s presentation using ordinary clinical evidence. AI changes how an interpretation may have been generated and reinforced. It should not change the evidentiary threshold clinicians apply to it.
What Clients Need to Know About Privacy and Confidentiality
Clients may experience a chatbot conversation as private because it happens alone, often on a personal device and in language that feels intimate. However, the privacy protections are very different from those of clinical care.
Consumer AI services set their own terms for data retention, use and sharing. The APA advises caution with sensitive information and with the use of AI for diagnosis or psychological test interpretation. Clinicians do not need to become experts in every platform’s privacy policy, but they should make sure clients understand that disclosure to a chatbot is not the same as disclosure within a confidential therapeutic relationship.
Practical guidance should be specific. Clients should think carefully before uploading identifiable health records, therapy material, session recordings, psychological test content or information about another person. If they choose to share sensitive material, they should first understand how the service may store, use or retain it.
The same restraint applies when a client offers to show the clinician a chatbot transcript. Start with the clinical reason for reviewing it. What happened in the interaction that matters for assessment, treatment or risk? A full transcript may contain highly sensitive disclosures, third-party information and material that has little clinical relevance. Review only what is needed.
Documentation should follow the same principle. Record the clinical significance of the AI use, any relevant risk, the guidance provided and the treatment response. Importing large sections of a chatbot transcript into the clinical record can create additional privacy exposure without improving care.
The practical rule is simple. Collect and retain only the AI-related information that the clinical work actually requires.
Why Clinicians Need to Assess AI Use Over Time
A single chatbot exchange may tell the clinician very little about the clinical significance of AI use. The more important pattern may only become visible over time.
A client may begin by occasionally asking for information or reassurance. The conversations can gradually become longer, more frequent or more emotionally important. The client may start returning to the system whenever uncertainty arises, relying on its interpretations, losing sleep during extended conversations or giving it increasing influence over relationships, treatment decisions or beliefs. None of those changes requires one obviously dangerous response.
Benjamin Nelson, Mark Kalinich and John Torous make this distinction in the JAMA Viewpoint “Specialized or General-Purpose—The Wrong Question for Mental Health AI Safety”. They distinguish harms that arise within a brief interaction from harms that accumulate across repeated use, including dependency, attributed sentience, romantic attachment and reinforcement of delusional themes.
For clinicians, this means asking about the pattern of use, not simply reviewing the most recent exchange:
How long has the client been using the system?
Has use become more frequent, prolonged or difficult to stop?
Has the chatbot become more emotionally important?
Is the client increasingly relying on it for reassurance, interpretation or decisions?
Is use displacing sleep, work, treatment or human relationships?
Have the client’s beliefs or behaviour changed alongside the interaction?
Returning to the composite client from the opening, one chatbot response about one suspicious text may carry little clinical significance. Concern grows if this client repeatedly brings new messages to the system, receives interpretations that reinforce the same suspicion and becomes increasingly certain without new evidence. Once that pattern begins to affect sleep, surveillance, confrontation or broader suspiciousness, the clinical picture has changed.
A client may therefore move from one level of concern to another as the pattern of AI use changes. Clinicians need to recognise that movement early enough to adjust assessment, treatment and risk management.
How Professional Responsibility Changes With AI Involvement
A clinician who discovers that a client uses AI is in a different position from one who recommends an AI tool or incorporates it into treatment. A practice that selects and deploys the technology takes on a different responsibility again.
When clients choose AI independently, the clinician’s responsibility concerns its clinical effects. If the use begins to influence symptoms, beliefs, treatment or risk, it should be assessed and managed as part of care. The clinician has not endorsed the system simply by discussing it with the client.
That changes when the clinician recommends a particular tool, asks the client to use it between sessions or relies on its output in treatment. The clinician has now given the technology professional weight. They should understand what it is intended to do, the evidence supporting that use, its important limitations and risks, whether it is appropriate for the particular client and what alternatives are available.
The boundary becomes clearer still when a practice selects and deploys AI for transcription, documentation, messaging, assessment support or another clinical function. The organisation is no longer responding to technology introduced by the client. It has introduced the technology into care and needs defined uses and limits, appropriate privacy and data governance, human oversight and a process for identifying and responding to errors.
Paulus and Torous make a related point in their JAMA Psychiatry Viewpoint. Clinical use, they argue, should occur within bounded tasks, approved workflows and an organisational framework that preserves professional accountability.
A useful practice position should therefore state where these boundaries lie. Encountering a client’s AI use, recommending AI and deploying AI are different forms of professional involvement, and clinicians should know when they have crossed from one to the next.
A Six-Step Response When Clients Bring AI Into Care
A practice position becomes useful when clinicians know what to do when AI enters the conversation. A routine question can open the discussion without implying that AI use is either problematic or endorsed.
Many people now use AI for information, emotional support, relationship advice or help between sessions. Has AI played any role in how you have understood or managed what you are dealing with?
If the answer is yes, the clinician can work through six steps.
Ask what the client is using and why. Identify the system where relevant, what the client uses it for and what they are hoping to get from the interaction. Normalise disclosure without implying surveillance or judgement.
Decide whether the use is clinically significant. Consider its function, frequency and consequences. Does it affect symptoms, beliefs, relationships, functioning, treatment participation, privacy or risk? Ordinary use does not require extensive assessment simply because AI is involved.
Separate experience from interpretation. Establish what actually happened, what the client concluded and what the AI system added. Treat AI-generated diagnoses, formulations and explanations as material to assess rather than evidence in themselves.
Assess the pattern over time. Ask whether use is becoming more frequent, prolonged, emotionally important or difficult to stop. Look for increasing reliance on the system for reassurance, interpretation or decisions, and for displacement of sleep, treatment, daily functioning or human relationships.
Match the response to the level of concern. Level one may require acknowledgement and brief guidance. Level two calls for active clinical work on the role AI is playing in the presenting problem or treatment. Level three requires direct assessment and established risk, psychiatric, safeguarding or emergency procedures.
Review and document proportionately. Do not collect an entire chatbot history simply because it exists. Review only the material needed for assessment, treatment or risk management. Document the clinical significance of the AI use, relevant risk, guidance or intervention and planned follow-up.
The clinician should also know when the nature of their own involvement changes. Discussing AI that a client chose independently does not amount to endorsing the product. Recommending a specific system, assigning an AI-supported activity or incorporating AI output into treatment gives the technology professional weight and requires greater knowledge of its intended use, evidence, limitations and suitability.
Practice leaders need to turn these clinical decisions into explicit organisational expectations. A written position should address:
how and when clinicians routinely ask about client AI use
the standard for determining when AI use becomes clinically significant
the three levels of response and thresholds for supervision or escalation
how clinicians distinguish client experience from AI-generated interpretation
when chatbot transcripts should be reviewed and how much should enter the clinical record
what privacy guidance clinicians should provide
what clinicians must know before recommending a specific AI product or AI-supported activity
the distinction between client-chosen, clinician-recommended and practice-deployed AI
disclosure and management of relevant financial or commercial relationships
approved and prohibited uses of AI by clinicians for case reflection, treatment planning, documentation or other clinical work
what client or clinical information may never be entered into unapproved systems
who provides consultation when an AI-related issue exceeds a clinician’s competence or raises an unfamiliar risk
who is responsible for reviewing the policy as products, evidence, professional guidance and regulation change
The policy also needs to cover everyone who can introduce AI-related risk into the service. Trainees and contractors require the same clinical and data-handling expectations as other practitioners, with appropriate supervision. Administrative staff need clear rules about approved systems, confidential information and what they may enter into AI tools.
Services working with children and adolescents require explicit additional provisions. Clinicians need guidance on how to ask about AI use developmentally, what confidentiality can be offered, when caregiver involvement becomes necessary and when AI-related behaviour raises a safeguarding concern. Consent, confidentiality and caregiver rights vary by developmental capacity and jurisdiction, so an adult protocol should not simply be carried across unchanged.
A written position should leave clinicians with fewer decisions to improvise when AI becomes clinically relevant. It should tell them what to ask, what to assess, what evidence to trust, what material to review, when to escalate and when their own use or recommendation of AI creates additional professional obligations.
Establish Your Position Before the Next Client Forces the Question
Returning to the client with the suspicious texts, a prepared clinician knows how to proceed. They establish what happened, separate observation from AI-generated interpretation, assess how repeated use has affected certainty and behaviour, review only what is clinically necessary, address privacy and act if the risk is escalating.
Client AI use is already part of routine mental health care. People are using these systems to make sense of symptoms, consider diagnoses, seek reassurance, interpret relationships and obtain support between sessions. Sometimes that use will be helpful or inconsequential. At other times it may reinforce symptoms, complicate treatment or contribute to risk. Clinicians need a consistent way to recognise the difference and respond appropriately.
Every practice now needs to decide what its clinicians should ask, what counts as clinically significant, when AI-generated material should be examined, how privacy should be handled, when risk requires escalation and what changes when the clinician or organisation introduces AI into care.
AI will keep entering clinical practice through the clients who use it. So the next time a client brings AI into the room, your clinicians should already know what your practice stands for and what they are expected to do.
Scott Wallace, PhD, is a behavioural scientist and mental health technology strategist trained in clinical psychology and neuropsychology. For more than 35 years, he has worked across clinical practice, digital product development and conversational systems, helping shape early digital mental health platforms, mobile interventions and NLP/NLG-based tools well before the current generation of large language models. He now advises founders, health systems and investors on AI-enabled mental health, with a focus on clinical safety, governance, product architecture and the unit economics of care.
By safeguarding, I mean the ordinary clinical steps a clinician would take when AI use is contributing to a significant safety concern, for example escalating suicidality, psychosis or mania, coercion or exploitation, threats toward another person, severe deterioration in functioning, or a level of dependency that is displacing sleep, treatment or human relationships
I'm curious what you consider "safeguarding" in this situation? I am assuming you are suggesting an addiction-framed working model for management?