Randomized Trial of a Generative AI Chatbot for Mental Health Treatment
Abstract
BACKGROUND Generative artificial intelligence (Gen-AI) chatbots hold promise for building highly personalized, effective mental health treatments at scale, while also addressing user engagement and retention issues common among digital therapeutics. We present a randomized controlled trial (RCT) testing an expert–fine-tuned Gen-AI–powered chatbot, Therabot, for mental health treatment. METHODS We conducted a national, randomized controlled trial of adults (N=210) with clinically significant symptoms of major depressive disorder (MDD), generalized anxiety disorder (GAD), or at clinically high risk for feeding and eating disorders (CHR-FED). Participants were randomly assigned to a 4-week Therabot intervention (N=106) or waitlist control (WLC; N=104). WLC participants received no app access during the study period but gained access after its conclusion (8 weeks). Participants were stratified into one of three groups based on mental health screening results: those with clinically significant symptoms of MDD, GAD, or CHR-FED. Primary outcomes were symptom changes from baseline to postintervention (4 weeks) and to follow-up (8 weeks). Secondary outcomes included user engagement, acceptability, and therapeutic alliance (i.e., the collaborative patient and therapist relationship). Cumulative-link mixed models examined differential changes. Cohen’s d effect sizes were unbounded and calculated based on the log-odds ratio, representing differential change between groups.
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These issues challenge the robustness of Heinz et al. [66].
“These issues challenge the robustness of Heinz et al. [66].”
From Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers. (2025) · cites this paper as [66]
Heinz et al.'s RCT screened out clients with active suicidal ideation, mania, and psychosis; used a second model to classify crisis; had clinicians review messages.
“Notably, they 1) screened out clients with active suicidal ideation, mania, and psychosis; 2) used a second model to classify if clients were in crisis; and 3) had clinicians manually review all messages sent by Therabot to correct any false medical advice and safety concerns.”
From Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers. (2025) · cites this paper as [66]
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Self-citationShaping the future of mental health: three decades of reform in the Arab world
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My love will be there forever: Ghostbots and the artificial continuing communication
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Personalizing Human-LLM Interactions through Mixed Profiling
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- Authors
- Heinz, M. V., Mackin, D. M., Trudeau, B. M., Bhattacharya, S., Wang, Y., Banta, H. A., Jewett, A. D., Salzhauer, A. J., Griffin, T. Z., & Jacobson, N. C.
- Affiliations
- Dartmouth College (US)
- ORCID
- Venue
- NEJM AI
- Publisher
- Massachusetts Medical Society
- Published
- 2025-03-27
- Volume
- 2
- Issue
- 4
- Pages
- AIoa2400802-AIoa2400802
- ISSN
- 2836-9386 (ISSN-L)
- OpenAlex
- W4408886672
- Semantic Scholar
- 999e64cf92fe72297e2011f6285180d4bc30f4b5
- Cited by
- 287
- References
- 36
- Citations by year
- 2026: 193 · 2025: 94
- FWCI
- 229.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NEJM AI (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- generative AI chatbots, psychotherapy, digital mental health interventions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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