Key takeaways- Commercial mental health chatbots show a modest effect on depression in pooled trials (g = −0.35, about 1.6 PHQ-9 points, below the 5-point minimal important difference the authors used). Anxiety effects were uncertain, and harms were rarely measured well. [1]
- Standalone mental health apps look smaller once you correct for publication bias. In 72 RCTs, depression effects dropped from g = 0.45 to 0.18 after adjustment. Sleep was the exception. [2]
- In one large RCT with university students, an AI agent beat group therapy on anxiety reduction. The students’ sense of alliance with the bot tracked their engagement and improvement. That is one population and one product, not proof that chatbots replace therapy. [3]
- AI scribes saved medical clinicians a few minutes per visit in EHR time and note time, but after-hours documentation did not change significantly. [5] In primary care, AI-scribed notes recorded more psychiatric symptoms but were linked to less documented psychiatric management. [6]
- The legal ground has moved. Several states now restrict AI in mental health care, and some require notice and consent before AI records or transcribes a visit. Check your own state and board before you turn on an AI note tool.
Why this matters this month
Two kinds of AI are showing up in therapy at the same time, and they raise different questions. The first is the chatbot your client opens at 2 a.m.: a general model like ChatGPT, or a product built and marketed for mental health. The second is the tool on your side of the room, an ambient scribe or note generator that listens to session audio or reads your shorthand and drafts the progress note.
Both are no longer hypothetical. In an August 2026 KFF poll, 16% of adults said they had used an AI tool for mental health information or advice in the past year, rising to 28% of adults under 30. In a large sample of US youth ages 4 to 17, measured through a parental monitoring app, 31.9% had used generative AI apps, and that rose to half of 15-to-17-year-olds, though median daily use was well under a minute. [7] On the clinician side, the r/therapists threads keep circling back to the same three questions about note-takers: is the vendor signing a BAA, what does the client consent to, and what happens to the audio.
This page walks through the research on each side, then the regulatory picture, then what it means for your sessions and your paperwork. We kept the numbers exactly as the authors reported them. When a finding is from medicine rather than psychotherapy, we say so.
What the chatbot trials found
The most useful single paper for a practicing clinician is a 2026 systematic review and meta-analysis of commercial mental health chatbots: the products clients can actually download, not lab prototypes. [1] The authors pooled 52 studies, 22 of them randomized trials, covering 13 chatbots.
The headline is modest. Depression improved against controls with a pooled Hedges g of −0.35. The authors translated that to about 1.6 points on the PHQ-9 and noted that this falls below the 5-point minimal clinically important difference they used. They also reported a number needed to treat of about 6.6. Anxiety effects were uncertain, with a confidence interval that crossed zero, and the authors wrote that net harm for anxiety could not be excluded. Loneliness improved slightly. Certainty of evidence ranged from low to very low.
Two details matter more than the effect sizes. First, completion rates in trials (about 79.5% pooled) probably overstate real-world retention. Second, only 3 of the 22 RCTs actively looked for harms. A trial that never asks about harm will report no harm. The authors concluded that these products are best positioned as low-intensity adjuncts, not replacements, and that they need safety built in from the start.
A broader 2025 meta-analysis of standalone mental health apps, not only AI chatbots, adds a sobering correction. [2] Across 72 RCTs and 21,702 participants compared with inactive controls, the effect on depression was g = 0.45, but it fell to 0.18 after adjustment for publication bias. Anxiety fell from 0.35 to 0.18. Sleep held at 0.71 after adjustment. The review found no pooled effect for suicidal ideation, self-injury, smoking, or alcohol use. An umbrella review in the same journal reached a similar place: certainty for digital mental health interventions was mostly low or very low. [8]
The trial that will get quoted
The study you will hear about at conferences is a three-arm RCT of 995 university students in psychological distress. They were randomized to a conversational AI agent, to group therapy, or to a waiting list. [3] After the intervention, the AI group showed greater anxiety reduction than both group therapy (mean difference −2.17) and the waiting list (−2.15). It also showed greater depression reduction than the waiting list (−1.99) and larger well-being gains than both comparison groups. PTSD symptoms did not differ.
The finding that should interest therapists most is the alliance finding. Students who felt a stronger working alliance with the AI engaged more (β = 0.31) and improved more (β = −0.58). That fits the 2026 meta-analysis of alliance in internet interventions, which found an alliance-outcome correlation of r = .21, strongest for agreement on tasks (.25), then goals (.19), then bond (.12). [4] People form something like an alliance with software, and it seems to matter. That is the opportunity and the risk in one sentence.
Keep the frame tight, though. The comparison was group therapy, not individual therapy. The sample was young, educated, and in one country. The outcomes were self-reported. And this was one product.
What the AI scribe studies found
No large trial yet tests AI scribes in outpatient psychotherapy specifically. The best current data come from medicine. They are still useful because the workflow is the same: a tool listens, drafts, and you sign.
In a 2025 cohort study at an academic health system, 125 ambulatory clinicians who used an AI scribe were compared with 478 covariate-balanced nonusers. [5] AI scribe use was associated with 8.5% lower mean EHR time per appointment (about 2.4 minutes) and 15.9% lower time in notes (about 1.8 minutes). There was no significant change in after-hours documentation, the so-called pajama time, and no change in appointment length or volume. That is a real but small gain. If your documentation pain is the evening catch-up, a scribe alone may not fix it. The habits around the scribe matter.
The more important 2026 paper, from JAMA Psychiatry, looked at what AI scribes do to the content of care. [6] Researchers compared 20,302 primary care annual-visit notes: AI-scribed, human-scribed, unscribed during the same period, and pre-deployment. AI-scribed notes contained more documented neuropsychiatric symptoms across all six domains studied. Yet AI-scribed visits were less likely to include a documented psychiatric intervention: a referral, new diagnosis, or antidepressant prescription (adjusted odds ratio 0.83). Human-scribed visits did not show that drop. The authors were careful to say they cannot yet tell whether this changes patient outcomes.
For therapists, the parallel is uncomfortable. A note can look more thorough and still record less clinical thinking. If the scribe writes a beautiful summary of what the client said, the parts only you can add are your formulation, your risk reasoning, and your plan. Those are exactly the parts that matter to a board, a payer, or a court.
The new state rules, in plain English
This section is orientation, not legal advice. Laws are changing quickly, and your licensing board’s guidance controls.
KFF’s August 2026 summary describes several state approaches. Illinois, Nevada, Tennessee, Vermont, and Rhode Island have laws restricting the use of AI in mental health care. Colorado and Vermont allow administrative uses. Utah has taken a disclosure-based approach. Rhode Island requires consent and notification when AI is used to document visits. Colorado’s HB26-1195, effective August 12, 2026, is the most detailed so far. Licensees may use AI for administrative or supplementary support only if they review its outputs. They must disclose in advance and get written informed consent before AI records or transcribes a session. They must tell clients about the law’s AI restrictions at first contact. The law also bars AI products from implying that their outputs come from a licensed professional, or that user data is protected like therapist-client confidentiality.
In California, AB 489 took effect January 1, 2026. It bars AI tools from using terms, letters, or design that suggest a licensed person is providing the care or advice when one is not. The Board of Psychology published an advisory on it. The practical read for clinicians: never let an AI product talk to clients in a way that implies it is you, and never market an AI feature as therapy delivered by a licensee.
A sensible baseline, even where the law is quiet, is what the strictest states now require: tell clients in writing, get consent before recording, let them say no without penalty, and know where the audio and transcript go.
What this means in session
Practical moves
- Ask about chatbot use the way you ask about sleep or alcohol. Make it routine and neutral. Clients often hide it because they expect judgment, or because they suspect you will feel replaced.
- Get curious about the alliance they have with it. The RCT data suggest the felt relationship with a bot drives engagement. [3] What does the bot give them that is hard to get from people, including you? That is clinical material.
- Watch for the sycophancy problem. General-purpose models tend to agree. For a client with OCD-style reassurance seeking, health anxiety, or escalating beliefs, a tool that always validates can be part of the loop. Ask to see an exchange if they are willing.
- Name the safety gap plainly. Most chatbot trials did not actively measure harm. [1] Make sure clients know the bot is not a crisis service, and that 988 and your after-hours plan are.
- If you use a scribe, write the clinical reasoning yourself. Let the tool draft the narrative, then add formulation, risk assessment, and plan in your own words. The primary care data are a warning about notes that describe more and decide less. [6]
A script some clinicians find useful at intake:
“A lot of people use AI apps or chatbots to think things through between sessions. No judgment either way. Do you, and if so, what do you use it for?”
And for your own documentation consent:
“I use a note tool that drafts my notes from our session. I review and sign everything. You can say no, now or anytime, and it won’t change your care.”
Our tool reviews follow a published methodology. For any AI documentation product, these are the questions we would ask before signing up:
- Will the vendor sign a BAA, and does it cover the AI subprocessor too?
- Is session audio stored, and for how long? Can you turn storage off?
- Is your data used to train models? Is that opt-in or opt-out?
- Does the tool support written client consent and an easy way to skip a session?
- Can you edit every section, and does the final note clearly read as yours?
Mentalyc is one AI note tool on our practice tools list. SimplePractice also offers built-in note features. If you try Mentalyc, this is our referral link.
Referral link: if you sign up, Mason may receive a commission or credit. It doesn’t cost you extra, and it is not a recommendation that the tool fits your practice. How referral links work.
If you want help writing an AI-use policy, a consent form, and a workflow your team will actually follow, Advantage Coaching does practice implementation work with clinicians.
The studies, in plain English
Each card links to the PubMed record. Numbers are reported the way the authors reported them. Read the abstract before you quote any of them.
[1]Psychother Psychosom · 2026Systematic review & meta-analysis
Commercial AI-Based Mental Health Chatbots as Low-Intensity Adjuncts to Psychotherapy: Effectiveness, Adherence, and Safety - A Systematic Review and Meta-Analysis
Yang H, Chang F, Muroi F et al.
52 studies (22 RCTs), 13 commercial chatbots. Depression g = −0.35 (95% CI −0.56 to −0.13), about 1.6 PHQ-9 points, below the 5-point MCID; NNT about 6.6. Anxiety uncertain (g = −0.37, CI crossing zero). Only 3 of 22 RCTs actively ascertained harms. Certainty low to very low.
[2]Lancet Digit Health · 2025Meta-analysis, 72 RCTs
Efficacy of standalone smartphone apps for mental health: an updated systematic review and meta-analysis
Kulke JK, Fuhrmann LM, Berking M et al.
Standalone mental health apps vs inactive controls, N = 21,702. Depression g 0.45, which became 0.18 after publication-bias adjustment; anxiety 0.35 became 0.18; sleep 0.71, unchanged. No pooled effect for suicidal ideation, self-injury, smoking, or alcohol.
[3]JAMA Netw Open · 2026Randomized clinical trial
Efficacy of a Conversational AI Agent for Psychiatric Symptoms and Digital Therapeutic Alliance: A Randomized Clinical Trial
Shoshani A, Gurfinkel B, Kor A et al.
995 university students: AI agent vs group therapy vs waiting list. The AI group reduced anxiety more than group therapy (MD −2.17) and the waiting list (MD −2.15), and reduced depression more than the waiting list (MD −1.99). PTSD did not differ. Perceived alliance was associated with engagement (β 0.31) and improvement (β −0.58).
[4]J Consult Clin Psychol · 2026Meta-analysis
Alliance in internet-based interventions: A systematic review and correlation multilevel meta-analysis
Flückiger C, Schweizer AM, Boehme MS et al.
Alliance in internet interventions. Overall alliance-outcome association r = .21; task .25, goal .19, bond .12.
[5]JAMA Netw Open · 2025Retrospective cohort
Use of an AI Scribe and Electronic Health Record Efficiency
Pearlman K, Wan W, Shah S et al.
125 AI scribe users vs 478 balanced nonusers (ambulatory medicine). 8.5% lower mean EHR time per appointment (about 2.4 min) and 15.9% lower note time (about 1.8 min). No significant change in after-hours documentation, appointment length, or volume.
[6]JAMA Psychiatry · 2026Matched retrospective cohort
Psychiatric Documentation and Management in Primary Care With Artificial Intelligence Scribe Use
Castro VM, McCoy TH, Verhaak P et al.
20,302 primary care notes. AI-scribed notes documented more neuropsychiatric symptoms, but were less likely to include a psychiatric intervention than unscribed visits (aOR 0.83, 95% CI 0.72–0.95). Human scribes showed no such difference.
[7]JAMA Netw Open · 2026Cross-sectional study
Generative Artificial Intelligence Applications Use Among US Youth
Maheux AJ, Akre-Bhide S, Boeldt D et al.
6,488 US youth ages 4–17 (parental monitoring app data). 31.9% used generative AI apps (50.4% at ages 15–17). Median use was about 0.18 minutes per day; the heaviest users exceeded 40 minutes per day.
[8]Lancet Digit Health · 2025Umbrella review
Digital health interventions for mental health disorders: an umbrella review of meta-analyses of randomised controlled trials
Crocamo C, Palpella D, Cavaleri D et al.
Digital mental health interventions. Certainty of evidence was mostly low or very low.
Other sources
Limitations
- Most chatbot evidence is low to very low certainty, and harms were rarely measured actively. [1]
- The headline RCT used one product with university students in one country, compared against group therapy and a waiting list, with self-report outcomes. [3]
- The AI scribe studies come from medical clinics, not psychotherapy. Both are observational, so they show associations, not causation. [5],[6]
- App effect sizes shrink sharply after publication-bias correction, which is a reason to discount single glowing trials. [2]
- Our summary of state laws is a snapshot from public sources as of October 2026. It is not legal advice. Confirm with your board, your malpractice carrier, or a health-law attorney.
Education for clinicians, not medical or legal advice. This page summarizes published research for licensed mental health professionals. It is not a treatment recommendation for any individual, and it does not replace your own clinical judgment, your licensing board’s rules, or consultation with a prescriber or attorney. Evidence reviewed through October 24, 2026. Spot an error? Tell us and we will fix it.