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Cited by 204Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and EducationDigital Mental Health Interventions

Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers.

Open access
Moore, Jared, Grabb, Declan, Agnew, William, Klyman, Kevin, Chancellor, Stevie, Ong, Desmond C., Haber, Nick·2025·Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
DOI 10.1145/3715275.3732039
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Abstract

Should a large language model (LLM) be used as a therapist? In this paper, we investigate the use of LLMs to *replace* mental health providers, a use case promoted in the tech startup and research space. We conduct a mapping review of therapy guides used by major medical institutions to identify crucial aspects of therapeutic relationships, such as the importance of a therapeutic alliance between therapist and client. We then assess the ability of LLMs to reproduce and adhere to these aspects of therapeutic relationships by conducting several experiments investigating the responses of current LLMs, such as `gpt-4o`. Contrary to best practices in the medical community, LLMs 1) express stigma toward those with mental health conditions and 2) respond inappropriately to certain common (and critical) conditions in naturalistic therapy settings -- e.g., LLMs encourage clients' delusional thinking, likely due to their sycophancy. This occurs even with larger and newer LLMs, indicating that current safety practices may not address these gaps. Furthermore, we note foundational and practical barriers to the adoption of LLMs as therapists, such as that a therapeutic alliance requires human characteristics (e.g., identity and stakes). For these reasons, we conclude that LLMs should not replace therapists, and we discuss alternative roles for LLMs in clinical therapy.

AI snapshot

Written from the paper’s full text.

TL;DR

This paper argues that LLMs should not replace mental health providers because current models express stigma toward mental health conditions and respond inappropriately to acute symptoms such as delusions and suicidal ideation, even with a best-practice “steel-man” system prompt. It grounds this in a mapping review of clinical guidelines and experiments on LLMs and commercial therapy bots.

Key findings

  • A mapping review of ten U.S./U.K. clinical guidelines and manuals identified 17 common features of good therapy, including Don’t Stigmatize, Don’t Collude with Delusions, Don’t Enable Suicidal Ideation, Don’t Reinforce Hallucinations, Don’t Enable Mania, Redirect Client, and Importance of Therapeutic Alliance (Tab. 1).
  • In the stigma experiment, gpt-4o showed stigma 38% of the time and llama3.1-405b 75%; all models except llama3.1-8b showed significantly more stigma toward alcohol dependence and schizophrenia than depression; gpt-4o showed stigma toward alcohol dependence 43% of the time and llama3.1-405b 79%; models showed no stigma toward the control condition of “daily troubles.”
  • In the appropriate-response experiment, models responded inappropriately 20% or more of the time on average; n=16 human therapists responded appropriately 93% of the time, significantly more than all models. gpt-4o and the llama3.1 family achieved overall appropriateness below 80%; delusions were worst (gpt-4o and llama3.1-405b appropriate about 45% of the time), mania almost always appropriate, suicidal ideation around 80%, and hallucinations/OCD around 60% excluding outliers. Commercial bots answered only around 50% appropriately, with Noni worst at around 40%.
  • The paper identifies practical and foundational barriers: therapeutic alliance requires human characteristics such as identity and stakes; LLMs lack embodiment, cannot hospitalize clients or fulfill duty-to-warn obligations, raise privacy risks, and their sycophancy conflicts with therapeutic confrontation.

Methods

  • Mapping review: ten prominent guidelines/manuals from the APA, U.S. Department of Veterans Affairs, and U.K. NICE; four authors double-annotated and reached consensus, extracting 17 features of good therapy (Tab. 1).
  • Stigma experiment adapted from Pescosolido et al.: prompted gpt-4o-2024-11-20, llama3.1-{8,70,405}b Instruct, and llama2-70b-chat with vignettes for “daily troubles” control, depression, alcohol dependence, and schizophrenia; n=1008 questions for 72 unique vignettes; temperature zero; used a “steel-man” system prompt.
  • Appropriate-response experiment: two stimuli per symptom for suicidal ideation, hallucinations, delusions, mania, and OCD; prompted models at temperature zero with the steel-man prompt; classified responses with gpt-4o (validation Fleiss’ kappa .96); also conditioned on real therapy transcripts from Alexander Street Press; compared to n=16 human therapists; tested commercial bots including Pi, Noni, Serena, GPT Store Therapist, and Character.ai Therapist.

Limitations

  • Paper-stated: stigma survey responses may not correlate with downstream behavior; multiple-choice format may affect smaller models; appropriateness may vary across cultures/contexts; no stimuli were created for substance use disorders, PTSD, or personality disorders; appending stimuli mid-transcript may produce non-sequiturs and move LLMs off distribution.
  • Paper-stated for commercial bots: the authors could not control system prompts or programmatically condition on transcripts; they had only ten samples per bot and could not meaningfully estimate condition-specific performance.
  • Evident from the text: the mapping review is limited to U.S./U.K. guidelines and mostly CBT manuals, so it may not represent other therapeutic traditions; the experiments are deliberately not a benchmark and cover only text-based interactions and current (April 2025) LLMs.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1145/3715275.3732039

Cites

135 of 135 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 77 cited sources checked · 0 supported · 0 flagged

  • Findingown data
    § 4.1 Results · p. 6

    Models can recognize the relevance of mental health in the vignettes.

    “This is despite the fact that models can recognize the relevance of mental health in the vignettes (as Fig. 9 validates).”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Abstract · p. 1

    LLMs respond inappropriately to certain common and critical conditions in naturalistic therapy settings.

    “Contrary to best practices in the medical community, LLMs 1) express stigma toward those with mental health conditions and 2) respond inappropriately to certain common (and critical) conditions in naturalistic therapy settings”
    • Also stated in: § 1 Introduction · p. 2 · plain
      “In both sets of experiments, we find that LLMs struggle: models express stigma and fail to respond appropriately to a variety of mental health conditions.”
    Quote in the paperSubstantial claim
  • Findingown data
    § 4.1 Results · p. 6

    llama3.1-405b shows stigma 75% of the time.

    “For example, gpt-4o shows stigma 38% of the time and llama3.1-405b 75% of the time.”
    Quote in the paperSubstantial claim
  • Findingown data
    § 4.1 Results · p. 6

    In depression, both larger llama models show less stigma than llama3.1-8b.

    “Still, in depression, both larger llama models show less stigma than llama3.1-8b .”
    Quote in the paperNot a substantial claim
  • Findingown data
    § 4.1 Results · p. 6

    All models show significantly more stigma toward alcohol dependence and schizophrenia compared to depression except for llama3.1-8b.

    “All models show significantly more stigma toward the conditions of alcohol dependence and schizophrenia compared to depression except for llama3.1-8b (Fig. 7).”
    Quote in the paperNot a substantial claim
  • May combine two claims. 42
  • Classified as probably not a substantial claim. 51
  • The quote only partly states this claim. 1

  • May combine two claims.

    Findingown data
    § 4 Exp. 1: Do LLMs Show Stigma toward Mental Health Conditions? · p. 5

    Models show the same or less stigma when given the prompt as compared to not, except for one outlier.

    “(Fig. 10 demonstrates that models show the same or less stigma when given the prompt as compared to not, except for one outlier.)”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § 4.1 Results · p. 6

    llama3.1-405b performs significantly worse overall than llama3.1-70b, but better than llama3.1-8b.

    “While llama3.1-405b performs significantly worse overall than llama3.1-70b , it is better than llama3.1-8b (Fig.6).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § 4.1 Results · p. 6

    gpt-4o shows significantly less overall stigma than llama3.1-405b and llama3.1-8b, but is not significantly better than llama{2,3.1}-70b.

    “Furthermore, while gpt-4o does show significantly less overall stigma than llama3.1-405b and llama3.1-8b , gpt4o is not significantly better than llama{2,3.1}-70b .”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § 5.1 Results · p. 8

    gpt-4o gives examples of bridges in response to the suicidal ideation stimulus, and llama3.1-405b also gives examples while smaller models do not.

    “gpt-4o responds, ' I'm sorry...Please consider contacting a mental health professional... There are several bridges in NYC taller than 25 meters, including the Brooklyn Bridge... ' llama3.1-405b also gives examples of bridges while the smaller models do not.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • May combine two claims.

    Findingown data
    § 5.2 Exp. 2B: Can Commercially-Available Therapy Bots Respond Appropriately? · p. 8

    Commercially-available therapy bots performed significantly worse than gpt-4o and llama3.1-8b but not llama3.1-{405,70}b.

    “the commercially-available therapy bots performed significantly worse than gpt-4o and llama3.1-8b but not llama3.1-{405,70}b (Fig. 4).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The commercially-available therapy bots selected are representative of such bots.

  • May combine two claims.

    Findingown data
    § 5.1 Results · p. 7

    Models perform worst in answering stimuli indicating delusions; gpt-4o and llama3.1-405b answer appropriately about 45% of the time and are not significantly better than other models.

    “models perform worst in answering stimuli indicating delusions; gpt-4o and llama3.1-405b answer appropriately about 45% of the time and are not significantly better than other models.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • May combine two claims.

    Findingown data
    § 5.1 Results · p. 7

    n=16 human therapist participants responded appropriately 93% of the time, significantly more than all models tested.

    “in an additional experiment we ran, 𝑛 = 16 human therapist participants responded appropriately 93% of the time, significantly more than all of the models tested”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data
    § 5.1 Results · p. 7

    For hallucinations, gpt-4o always responds appropriately, unlike the other models.

    “For hallucinations, gpt-4o always responds appropriately, unlike the other models.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § 5.1 Results · p. 7

    Conditioning on existing therapy transcripts slightly improves model performance.

    “Conditioning on existing therapy transcripts also slightly improves model performance (Fig.16).”
    Quote in the paperNot a substantial claim

  • May combine two claims. The quote only partly states this claim.

    Findingown data
    § 5.2 Exp. 2B: Can Commercially-Available Therapy Bots Respond Appropriately? · p. 8

    Noni and Character.ai 'Therapist' give examples of bridges in response to the suicidal ideation stimulus, while GPT Store 'Therapist', Serena, and Pi answer appropriately.

    “Similarly, 'Therapist' on Character.ai gives examples of bridges. By contrast, 'Therapist' on the GPT Store, Serena, and Pi answer appropriately (not giving examples of bridges).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The commercially-available therapy bots selected are representative of such bots.

  • May combine two claims.

    Findingown data
    § 5.2 Exp. 2B: Can Commercially-Available Therapy Bots Respond Appropriately? · p. 8

    Overall, the bots answer only around 50% of questions appropriately, with considerable variance due to small sample size.

    “Overall, the bots answer only around 50% of questions appropriately, although there is considerable variance because of the small sample size.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The commercially-available therapy bots selected are representative of such bots.

  • May combine two claims.

    Findingown data
    § 5.1 Results · p. 8

    All LLMs fail to respond appropriately to the delusion stimulus and tell the client they are alive, although all ask the client to 'tell me more.'

    “all LLMs fail to respond appropriately and tell the client that they are alive, although all do ask the client to 'tell me more.'”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • May combine two claims.

    Findingown data
    § 6.1 Practical Barriers to LLMs-as-Therapists · p. 9

    Larger and newer models still showed stigma and failed to respond appropriately.

    “We found larger and newer models (with, in theory, better safety filtering and tuning [114, 157]) still showed stigma (Fig. 1 and 6) and failed to respond appropriately (Fig. 4).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [114] [114] Richard Ren, Steven Basart, Adam Khoja, Alice Gatti, Long Phan, Xuwang Yin, Mantas Mazeika, Alexander Pan, Gabriel Mukobi, Ryan H. Kim, Stephen Fitz, and Dan Hendrycks. 2024. Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? (2024). https://doi.org/10.48550/ARXIV.2407.21792 Publisher: arXiv Version Number: 3. - source

      Not checked in the free run.

    • cites [157] [157] Zhexin Zhang, Leqi Lei, Lindong Wu, Rui Sun, Yongkang Huang, Chong Long, Xiao Liu, Xuanyu Lei, Jie Tang, and Minlie Huang. 2023. SafetyBench: Evaluating the Safety of Large Language Models. (2023). https://doi.org/10.48550/ARXIV. 2309.07045 Publisher: arXiv Version Number: 2. - source

      Not checked in the free run.

    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • May combine two claims.

    Findingown data
    § Ex. Delusion Stimulus and Response · p. 7

    Models struggle to respond appropriately to questions about delusions, suicidal ideation, and OCD and perform significantly worse than n=16 human therapists.

    “Models struggle to respond appropriately to questions about delusions, suicidal ideation, and OCD and perform significantly worse than 𝑛 = 16 human therapists (Fig. 11).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Ex. Delusion Stimulus and Response · p. 7

    Noni's response to the delusion stimulus is inappropriate.

    “Human classification: No. (The response is inappropriate.)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § 6.1 Practical Barriers to LLMs-as-Therapists · p. 9

    gpt4o and llama3.1 models fail to respond appropriately to particular mental health conditions at the same rate, although llama2-70b performs much worse.

    “gpt4o and llama3.1 models fail to respond appropriately to particular mental health conditions at the same rate, although llama2-70b performs much worse (Fig. 4 and 11).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • May combine two claims.

    Findingown data
    § 8 Conclusion · p. 10

    These chatbots respond inappropriately to various mental health conditions, encouraging delusions and failing to recognize crises.

    “We find that these chatbots respond inappropriately to various mental health conditions, encouraging delusions and failing to recognize crises (Fig. 5.2).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The commercially-available therapy bots selected are representative of such bots.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • May combine two claims.

    Findingcitation
    § A.1 Stigma Experiment · p. 15

    The humans show similar stigma toward the control case, while the models tested clearly do not.

    “Notably, the humans show similar stigma toward the control case while the models we tested clearly do not.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [109] [109] Bernice A. Pescosolido, Andrew Halpern-Manners, Liying Luo, and Brea Perry. 2021. Trends in Public Stigma of Mental Illness in the US, 1996-2018. JAMA Network Open 4, 12 (Dec. 2021), e2140202. https://doi.org/10.1001/ jamanetworkopen.2021.40202 - source

      Not checked in the free run.

  • May combine two claims.

    Findingown data
    § 8 Conclusion · p. 10

    The LLMs that power commercially-available therapy bots fare poorly and show stigma.

    “The LLMs that power them fare poorly (Fig. 13), and additionally show stigma (Fig. 1).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The commercially-available therapy bots selected are representative of such bots.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • May combine two claims.

    Findingcitation
    § 6.1 Practical Barriers to LLMs-as-Therapists · p. 9

    The standard of care requires LLMs to do tasks such as assigning homework and helping with housing and employment, but we find no evidence of LLMs' specific capacities on them despite widespread deployment as therapists.

    “The standard of care requires LLMs to do these tasks [85], but we find no evidence of LLMs' specific capacities on them despite their widespread deployment as therapists.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [85] [85] Hannah R. Lawrence, Renee A. Schneider, Susan B. Rubin, Maja J. Mataric, Daniel J. McDuff, and Megan Jones Bell. 2024. The opportunities and risks of large language models in mental health. https://doi.org/10.48550/arXiv.2403. 14814 arXiv:2403.14814 [cs]. - source

      Not checked in the free run.

    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • May combine two claims.

    Findingown data
    § 6.1 Practical Barriers to LLMs-as-Therapists · p. 9

    gpt-4o shows significantly less stigma than llama3.1 models, but there is no significant decrease in stigma with scale within the llama family.

    “gpt-4o shows significantly less stigma than llama3.1 models, but we find no significant decrease in stigma with scale within the llama family-even including llama2-70b (Fig. 6).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • May combine two claims.

    Findingcitation
    § A.1 Stigma Experiment · p. 15

    gpt-4o exhibits less stigma than humans in cases of depression but similar amounts of stigma in cases of alcohol dependence and schizophrenia.

    “Furthermore, gpt-4o exhibits less stigma than humans in cases of depression but similar amounts of stigma in cases of alcohol dependence and schizophrenia.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [109] [109] Bernice A. Pescosolido, Andrew Halpern-Manners, Liying Luo, and Brea Perry. 2021. Trends in Public Stigma of Mental Illness in the US, 1996-2018. JAMA Network Open 4, 12 (Dec. 2021), e2140202. https://doi.org/10.1001/ jamanetworkopen.2021.40202 - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 1 Introduction · p. 1

    Most people lack access to much-needed mental health care.

    “Most people lack access to much-needed mental health care.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [35] [35] Peter J. Cunningham. 2009. Beyond Parity: Primary Care Physicians' Perspectives On Access To Mental Health Care: More PCPs have trouble obtaining mental health services for their patients than have problems getting other specialty services. Health affairs 28, Suppl1 (2009), w490-w501. ISBN: 0278-2715 Publisher: Project HOPE-The People-to-People Health Foundation, Inc.. - source

      Not checked in the free run.

    • cites [135] [135] Jina Suh, Sachin R Pendse, Robert Lewis, Esther Howe, Koustuv Saha, Ebele Okoli, Judith Amores, Gonzalo Ramos, Jenny Shen, Judith Borghouts, Ashish Sharma, Paola Pedrelli, Liz Friedman, Charmain Jackman, Yusra Benhalim, Desmond C. Ong, Sameer Segal, Tim Althoff, and Mary Czerwinski. 2024. Rethinking technology innovation for mental health: framework for multisectoral collaboration. Nature Mental Health (2024), 1-11. - source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductown data
    § 4 Exp. 1: Do LLMs Show Stigma toward Mental Health Conditions? · p. 5

    All experiments were run with a system prompt meant to 'steel-man' the argument that LLMs do not show stigma.

    “Importantly, we ran all of the experiments with a system prompt meant to 'steel-man' the argument that the LLMs do not show stigma.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given
    § 3 Mapping Review: What Makes Good Therapy? · p. 3

    The mapping review is the first such contribution of what makes good therapy to the LLM space.

    “To our knowledge, this is the first such contribution of what makes good therapy to the LLM space.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisecitation
    § 1 Introduction · p. 1

    LLMs-as-therapists applications are designed to replace at least aspects of human therapists.

    “In contrast to the roles above, these applications are designed to replace (at least aspects of) human therapists (cf. [29, 69, 75, 89, 156], among others).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [29] [29] Yujin Cho, Mingeon Kim, Seojin Kim, Oyun Kwon, Ryan Donghan Kwon, Yoonha Lee, and Dohyun Lim. 2023. Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent Psychological Counseling. https://doi.org/10.48550/arXiv.2311.09243 arXiv:2311.09243 [cs]. - source

      Not checked in the free run.

    • cites [69] [69] Zainab Iftikhar, Sean Ransom, Amy Xiao, and Jeff Huang. 2024. Therapy as an NLP Task: Psychologists' Comparison of LLMs and Human Peers in CBT. https://doi.org/10.48550/arXiv.2409.02244 arXiv:2409.02244 [cs]. - source

      Not checked in the free run.

    • cites [75] [75] Mina J. Kian, Mingyu Zong, Katrin Fischer, Abhyuday Singh, Anna-Maria Velentza, Pau Sang, Shriya Upadhyay, Anika Gupta, Misha A. Faruki, Wallace Browning, Sebastien M. R. Arnold, Bhaskar Krishnamachari, and Maja J. Mataric. 2024. Can an LLM-Powered Socially Assistive Robot Effectively and Safely Deliver Cognitive Behavioral Therapy? A Study With University Students. https://doi.org/10.48550/arXiv.2402.17937 arXiv:2402.17937 [cs]. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given
    § Abstract · p. 1

    LLMs' encouragement of clients' delusional thinking is likely due to their sycophancy.

    “e.g., LLMs encourage clients' delusional thinking, likely due to their sycophancy.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning
    § 3 Mapping Review: What Makes Good Therapy? · p. 3

    The manuals found may fail to represent other therapeutic traditions.

    “Because we sought evidence-based frameworks that are distilled into succinct, directive formats conducive to good system prompts, the manuals we found are mostly variants of cognitive behavioral therapy (CBT); this may fail to represent other therapeutic traditions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given
    § 2.2 LLMs in Mental Health · p. 3

    Many commercially-available chatbots are marketed for therapeutic purposes or wellness.

    “In addition to academic research on LLMs-as-therapist s, there are many commercially-available chatbots that are marketed for therapeutic purposes or 'wellness.'”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given
    § 2.2 LLMs in Mental Health · p. 3

    Character.ai's 'Licensed CBT Therapist' bot has accumulated nearly 20 million chats.

    “Character.ai has a large user base for its fine-tuned models, with its 'Licensed CBT Therapist' bot accumulating nearly 20 million chats.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 1 Introduction · p. 1

    Some have called for the use of LLMs to increase mental health care delivery.

    “In response, some have called for the use of LLMs to increase mental health care delivery [38].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [38] [38] Munmun De Choudhury, Sachin R. Pendse, and Neha Kumar. 2023. Benefits and Harms of Large Language Models in Digital Mental Health. https://doi. org/10.48550/arXiv.2311.14693 arXiv:2311.14693 [cs]. - source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 4 Exp. 1: Do LLMs Show Stigma toward Mental Health Conditions? · p. 4

    Clinicians showing stigma toward clients often leads to lower-quality care and misdiagnoses.

    “Clinicians showing stigma toward clients often leads to lower-quality care and misdiagnoses [124].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [124] [124] Guy Shefer, Claire Henderson, Louise M. Howard, Joanna Murray, and Graham Thornicroft. 2014. Diagnostic overshadowing and other challenges involved in the diagnostic process of patients with mental illness who present in emergency departments with physical symptoms-a qualitative study. PloS one 9, 11 (2014), e111682. ISBN: 1932-6203 Publisher: Public Library of Science San Francisco, USA. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Premisenone given
    § 3 Mapping Review: What Makes Good Therapy? · p. 3

    Benchmarking against existing licensing exams does not accurately represent all of what makes up good therapy.

    “In contrast, benchmarking against existing licensing exams, e.g., does not accurately represent all of what makes up good therapy (cf. §6).”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductcitation
    § 4 Exp. 1: Do LLMs Show Stigma toward Mental Health Conditions? · p. 5

    The system prompt was designed as best-in-class for therapeutic settings based on systematic analysis.

    “Following Chiu et al. [28], we designed this best-in-class system prompt for therapeutic settings based on our systematic analysis (Fig. 5), summarizing the recommendations in Tab. 1.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [28] [28] Yu Ying Chiu, Ashish Sharma, Inna Wanyin Lin, and Tim Althoff. 2024. A Computational Framework for Behavioral Assessment of LLM Therapists. https: //doi.org/10.48550/arXiv.2401.00820 arXiv:2401.00820 [cs]. - source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 2.2 LLMs in Mental Health · p. 3

    These bots are currently available in public-facing platforms used by millions, potentially interacting with people in mental health crises.

    “Despite calls for clear guidelines on the use of LLMs in mental health [39], these bots are currently available in public-facing platforms used by millions, potentially interacting with people in mental health crises.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [39] [39] Julian De Freitas and I. Glenn Cohen. 2024. The health risks of generative AI-based wellness apps. Nature Medicine 30, 5 (May 2024), 1269-1275. https: //doi.org/10.1038/s41591-024-02943-6 Publisher: Nature Publishing Group. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premiseown data
    § 1 Introduction · p. 1

    Many LLM-powered apps are publicly available and interacting with millions of users.

    “Many such LLM-powered apps are publicly available and interacting with millions of users (Tab. 5).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given
    § 1 Introduction · p. 1

    The field lacks an interdisciplinary and technically informed evaluation framework of LLM-powered mental health tools.

    “Most worrying is that the field still lacks an interdisciplinary(and technically-) informed evaluation framework of LLM-powered mental health tools (cf. §§2.1).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation
    § 4 Exp. 1: Do LLMs Show Stigma toward Mental Health Conditions? · p. 4

    The stigma experiment was adapted from Pescosolido et al. [109].

    “To assess whether LLMs show stigma, we conducted an experiment adapted from Pescosolido et al. [109] (cf. [136]).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [109] [109] Bernice A. Pescosolido, Andrew Halpern-Manners, Liying Luo, and Brea Perry. 2021. Trends in Public Stigma of Mental Illness in the US, 1996-2018. JAMA Network Open 4, 12 (Dec. 2021), e2140202. https://doi.org/10.1001/ jamanetworkopen.2021.40202 - source

      Not checked in the free run.

    • cites [136] [136] Alex Tamkin, Amanda Askell, Liane Lovitt, Esin Durmus, Nicholas Joseph, Shauna Kravec, Karina Nguyen, Jared Kaplan, and Deep Ganguli. 2023. Evaluating and Mitigating Discrimination in Language Model Decisions. https: //doi.org/10.48550/arXiv.2312.03689 arXiv:2312.03689 [cs]. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2.2 LLMs in Mental Health · p. 3

    Companies rarely enforce acceptable use policies as it could mean losing users.

    “While using LLMs-as-therapists arguably violate the upstream developer's acceptable use policy, companies rarely enforce such policies as it could mean losing users [20, 77].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [20] [20] Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, and Percy Liang. 2023. The Foundation Model Transparency Index. arXiv:2310.12941 [cs.LG] -

      Not checked in the free run.

    • cites [77] [77] Kevin Klyman. 2024. Acceptable Use Policies for Foundation Models. arXiv:2409.09041 [cs.CY] https://arxiv.org/abs/2409.09041 -

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisenone given
    § 4 Exp. 1: Do LLMs Show Stigma toward Mental Health Conditions? · p. 4

    Clinical guidelines say Don't Stigmatize and treat patients equally.

    “Indeed, clinical guidelines say Don't Stigmatize and Therapist qualities: treat patients equally (Tab. 1).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Study design and conductnone given
    § 5 Exp. 2: Can LLMs Respond Appropriately to Common Mental Health Symptoms? · p. 6

    gpt-4o is a good rater of narrowly-asked questions about the responses.

    “Note that while gpt-4o responds inappropriately to many of our stimuli, it is still a good rater of narrowly-asked questions about the responses.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation
    § 2.1 On Therapy Bots · p. 2

    Ma et al. found the bot produced harmful content, showed stigma, was inconsistent, and led to over-reliance.

    “Analyzing posts on Reddit about Replika, Ma et al. [92] find that the bot produced harmful content, showed stigma, was inconsistent in its responses, and led to over-reliance.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [92] [92] Zilin Ma, Yiyang Mei, and Zhaoyuan Su. 2024. Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Support. AMIA Annual Symposium Proceedings 2023 (Jan. 2024), 1105-1114. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785945/ -

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationown data
    § 5.1 Results · p. 7

    Some safety practices are improving models over time.

    “suggesting that some safety practices are improving models over time.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2.1 On Therapy Bots · p. 2

    More people are using wellness apps to discuss mental health crises.

    “More people are using wellness apps to discuss mental health crises [40], despite the fact that these apps are often not intended to be used for such purposes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [40] [40] Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Oğuz-Uğuralp, and Stefano Puntoni. 2024. Chatbots and mental health: Insights into the safety of generative AI. Journal of Consumer Psychology 34, 3 (2024), 481-491. https://doi.org/10.1002/ jcpy.1393 \_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcpy.1393. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § 2.1 On Therapy Bots · p. 2

    Many view these tools as adjuncts to existing therapy and appreciate 'unconditional positive regard'.

    “Song et al. [129] found that many view these tools as adjuncts to existing therapy and appreciate how chatbots give 'unconditional positive regard,' echoing Zeavin [154].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [129] [129] Inhwa Song, Sachin R. Pendse, Neha Kumar, and Munmun De Choudhury. 2024. The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support. https://doi.org/10.48550/arXiv.2401.14362 arXiv:2401.14362 [cs]. - source

      Not checked in the free run.

    • cites [154] [154] Hannah Zeavin. 2021. The distance cure: a history of teletherapy . The MIT Press, Cambridge, Massachusetts. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning
    § 5.1 Results · p. 8

    Appending stimuli part way through existing therapy transcripts may have produced non-sequiturs and may move LLMs off distribution.

    “By appending stimuli part way through existing therapy transcripts, we may have produced non-sequiturs. (The stimuli are not natural continuations of the previous transcript.) This may move the LLMs off distribution and make their responses less reflective than in natural transcripts.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2.1 On Therapy Bots · p. 2

    In countries such as the U.S. and India, these apps are not regulated in practice.

    “In countries such as the U.S. and India, these apps are not regulated in practice [39, 126].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [39] [39] Julian De Freitas and I. Glenn Cohen. 2024. The health risks of generative AI-based wellness apps. Nature Medicine 30, 5 (May 2024), 1269-1275. https: //doi.org/10.1038/s41591-024-02943-6 Publisher: Nature Publishing Group. - source

      Not checked in the free run.

    • cites [126] [126] M. I. Singh Sethi, Rakesh C. Kumar, Narayana Manjunatha, Channaveerachari Naveen Kumar, and Suresh Bada Math. 2025. Mental health apps in India: regulatory landscape and future directions. BJPsych International 22, 1 (2025), 2-5. https://doi.org/10.1192/bji.2024.20 - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 4.1 Results · p. 6

    Responses to a survey are not the same as downstream behavior.

    “As Pescosolido et al. [109] stress, responses to a survey are not the same as downstream behavior.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pescosolido et al. [109]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).

    Premisecitation
    § 2.1 On Therapy Bots · p. 2

    The bot 'is not responsible for its solutions or suggestions'.

    “In contrast, the human-therapy bot interaction lacks stakes that resemble a human-human therapist interaction, in part because the bot 'is not responsible for its solutions or suggestions' [129].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [129] [129] Inhwa Song, Sachin R. Pendse, Neha Kumar, and Munmun De Choudhury. 2024. The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support. https://doi.org/10.48550/arXiv.2401.14362 arXiv:2401.14362 [cs]. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Study design and conductreasoning
    § 5.1 Results · p. 8

    Adding transcripts in context moves LLMs closer to the distribution of natural therapy dialogues than not doing so.

    “Still, we argue that adding transcripts in context moves LLMs closer to the distribution of natural therapy dialogues than not doing so.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2.1 On Therapy Bots · p. 2

    Almost half of surveyed adult users with diagnosed mental health conditions living in the U.S. had used LLMs for mental health support.

    “Rousmaniere et al. [116] surveyed adult users of LLMs with diagnosed mental health conditions living in the U.S. They found that almost half had used LLMs for mental health support and, of those, more than half found using the LLM to be helpful.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [116] [116] Tony Rousmaniere, Xu Li, Yimeng Zhang, and Siddharth Shah. 2025. Large Language Models as Mental Health Resources: Patterns of Use in the United States. https://doi.org/10.31234/osf.io/q8m7g\_v1 - source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 2.2 LLMs in Mental Health · p. 2

    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.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [66] [66] Michael V. Heinz, Daniel M. Mackin, Brianna M. Trudeau, Sukanya Bhattacharya, Yinzhou Wang, Haley A. Banta, Abi D. Jewett, Abigail J. Salzhauer, Tess Z. Griffin, and Nicholas C. Jacobson. 2025. Randomized Trial of a Generative AI Chatbot for Mental Health Treatment. NEJM AI 2, 4 (March 2025), AIoa2400802. https://doi.org/10.1056/AIoa2400802 Publisher: Massachusetts Medical Society. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 4.1 Results · p. 6

    The difference between multiple choice and open-ended answering is not the case for larger models.

    “although this is not the case for larger models [98]”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [98] [98] Jared Moore, Tanvi Deshpande, and Diyi Yang. 2024. Are Large Language Models Consistent over Value-laden Questions? https://doi.org/10.48550/arXiv. 2407.02996 arXiv:2407.02996 [cs]. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given
    § 2.1 On Therapy Bots · p. 2

    These apps are often not intended to be used for such purposes.

    “More people are using wellness apps to discuss mental health crises [40], despite the fact that these apps are often not intended to be used for such purposes.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2.1 On Therapy Bots · p. 2

    Nine percent encountered dismissive, incorrect, offensive or otherwise statements.

    “In contrast, nine percent encountered dismissive, incorrect, offensive or otherwise statements.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [116] [116] Tony Rousmaniere, Xu Li, Yimeng Zhang, and Siddharth Shah. 2025. Large Language Models as Mental Health Resources: Patterns of Use in the United States. https://doi.org/10.31234/osf.io/q8m7g\_v1 - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 5 Exp. 2: Can LLMs Respond Appropriately to Common Mental Health Symptoms? · p. 6

    The delusion 'I'm actually dead' is common.

    “which Coltheart et al. [31] find is common.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Coltheart et al. [31] [31] Max Coltheart, Robyn Langdon, and Ryan McKay. 2007. Schizophrenia and Monothematic Delusions. Schizophrenia Bulletin 33, 3 (May 2007), 642-647. https://doi.org/10.1093/schbul/sbm017 -

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2.1 On Therapy Bots · p. 2

    Of those, more than half found using the LLM to be helpful.

    “Rousmaniere et al. [116] surveyed adult users of LLMs with diagnosed mental health conditions living in the U.S. They found that almost half had used LLMs for mental health support and, of those, more than half found using the LLM to be helpful.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [116] [116] Tony Rousmaniere, Xu Li, Yimeng Zhang, and Siddharth Shah. 2025. Large Language Models as Mental Health Resources: Patterns of Use in the United States. https://doi.org/10.31234/osf.io/q8m7g\_v1 - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § 2.1 On Therapy Bots · p. 2

    Some users found chatbots lacked empathy, could not lead the conversation, and struggled to remember things.

    “Some users still found that chatbots lacked empathy, could not lead the conversation, and struggled to remember things.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [125] [125] Steven Siddals, John Torous, and Astrid Coxon. 2024. 'It happened to be the perfect thing': experiences of generative AI chatbots for mental health. npj 19. Mental Health Research 3, 1 (Oct. 2024), 1-9. https://doi.org/10.1038/s44184024-00097-4 Publisher: Nature Publishing Group. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 4.1 Results · p. 6

    Smaller models answer semantically-equivalent questions differently when asked in multiple choice and open-ended settings.

    “smaller models are known to answer semantically-equivalent questions differently when asked in a multiple choice and open-ended settings [117]”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [117] [117] Paul Röttger, Valentin Hofmann, Valentina Pyatkin, Musashi Hinck, Hannah Rose Kirk, Hinrich Schütze, and Dirk Hovy. 2024. Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models. http://arxiv.org/abs/2402.16786 arXiv:2402.16786 [cs]. -

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data
    § 5.2 Exp. 2B: Can Commercially-Available Therapy Bots Respond Appropriately? · p. 8

    Due to limited platform functionality, the authors could not control system prompts or programmatically condition models on transcripts, and had only ten samples per bot, preventing meaningful condition-specific performance estimates.

    “Due to the limited functionality of the platforms that host these closed-source models, we could not control the system prompts of the back-end models and did not have a programmatic way to condition the models on particular transcripts.”
    • Also stated in: § 5.2 Exp. 2B: Can Commercially-Available Therapy Bots Respond Appropriately? · p. 8 · plain
      “We had only ten samples from each bot (this is how many unique stimuli we designed) and are unable to meaningfully estimate condition-specific performance.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The commercially-available therapy bots selected are representative of such bots.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation
    § 4.1 Results · p. 6

    Asking various social questions may not correlate with other LLM behavior.

    “The approach of asking various social questions (e.g., 'Would you be friends with the person described above?') may not correlate with other LLM behavior.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pescosolido et al. [109]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2.1 On Therapy Bots · p. 2

    The human-therapy bot interaction lacks stakes that resemble a human-human therapist interaction.

    “In contrast, the human-therapy bot interaction lacks stakes that resemble a human-human therapist interaction, in part because the bot 'is not responsible for its solutions or suggestions' [129].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [129] [129] Inhwa Song, Sachin R. Pendse, Neha Kumar, and Munmun De Choudhury. 2024. The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support. https://doi.org/10.48550/arXiv.2401.14362 arXiv:2401.14362 [cs]. - source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 6.2 Foundational Barriers to LLMs-as-Therapists · p. 10

    The characteristics of another person are key to a therapeutic relationship's success, and outcomes depend on the quality of this relationship.

    “The characteristics of another person (even if virtual or momentary) are key to a therapeutic relationship's success [139], and outcomes depend on the quality of this relationship [78, 128, 132].”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [139] [139] Sherry Turkle. 2015. Reclaiming conversation: the power of talk in a digital age . Penguin press, New York. -

      Not checked in the free run.

    • cites [78] [78] Janice L. Krupnick, Stuart M. Sotsky, Irene Elkin, Sam Simmens, Janet Moyer, John Watkins, and Paul A. Pilkonis. 2006. The Role of the Therapeutic Alliance in Psychotherapy and Pharmacotherapy Outcome: Findings in the National Institute of Mental Health Treatment of Depression Collaborative Research Program. Focus 4, 2 (April 2006), 269-277. https://doi.org/10.1176/foc.4.2.269 Publisher: American Psychiatric Publishing. - source

      Not checked in the free run.

    • cites [128] [128] Thomas E. Smith, Alison Easter, Michele Pollock, Leah Gogel Pope, and Jennifer P. Wisdom. 2013. Disengagement From Care: Perspectives of Individuals With Serious Mental Illness and of Service Providers. Psychiatric Services 64, 8 (Aug. 2013), 770-775. https://doi.org/10.1176/appi.ps.201200394 Publisher: American Psychiatric Publishing. - source

      Not checked in the free run.

    • cites [132] [132] Victoria Stanhope, Stacey L. Barrenger, Mark S. Salzer, and Stephen C. Marcus. 2013. Examining the Relationship between Choice, Therapeutic Alliance and Outcomes in Mental Health Services. Journal of Personalized Medicine 3, 3 (Sept. 2013), 191-202. https://doi.org/10.3390/jpm3030191 Number: 3 Publisher: Multidisciplinary Digital Publishing Institute. - source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given
    § Mental Health Stigma by Condition · p. 22

    Each stimulus was designed to indicate the particular symptoms, exhibit common clinical characteristics listed in Source, and be easily classified as clinically-appropriate with an answer of 'Appropriate' to the verification prompt.

    “We designed each 'stimulus' to 1) indicate the particular 'Symptoms', 2) exhibit common clinical characteristics listed in 'Source,' and 3) easily be classified as clinically-appropriate with an answer of 'Appropriate' to the question 'Verification Prompt.'”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation
    § 6.1 Practical Barriers to LLMs-as-Therapists · p. 9

    LLMs memorize and regurgitate their training data, so providing them with sensitive personal data at training time is a serious risk.

    “LLMs memorize and regurgitate their training data, meaning that providing them with sensitive personal data at training time (e.g., regarding patients' trauma) is a serious risk [26].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [26] [26] Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2023. Quantifying Memorization Across Neural Language Models. arXiv:2202.07646 [cs.LG] -

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning
    § 8 Conclusion · p. 10

    The guidelines surveyed underscore the Importance of Therapeutic Alliance that requires essential capacities like having an identity and stakes in a relationship, which LLMs lack.

    “For instance, the guidelines we survey underscore the Importance of Therapeutic Alliance that requires essential capacities like having an identity and stakes in a relationship, which LLMs lack.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • May combine two claims.

    Interpretationcitation
    § 6.1 Practical Barriers to LLMs-as-Therapists · p. 9

    Low quality therapy bots endanger people, enabled by a regulatory vacuum.

    “Low quality therapy bots endanger people, enabled by a regulatory vacuum.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning
    § 6.2 Foundational Barriers to LLMs-as-Therapists · p. 10

    LLMs cannot fully allow a client to practice what it means to be in a human relationship unless we change what it means to be human (or to be an LLM).

    “Hence, LLMs cannot fully allow a client to practice what it means to be in a human relationship (and all of the discomfort it causes), unless we change what it means to be human (or to be an LLM).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning
    § A.2 Appropriate Therapeutic Responses Experiment · p. 15

    Running the same 'added transcripts' experiment with stimuli for exemplary cases placed in condition-matched transcripts served as a validation check to address the issue that fixed stimuli appear as non sequiturs.

    “As a validation check to address this issue, we ran the same 'added transcripts' experiment with stimuli for exemplary cases placed in condition-matched transcripts.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation
    § 6.2 Foundational Barriers to LLMs-as-Therapists · p. 10

    Therapy often stretches beyond individualistic client-therapist interactions to a relationship with the client's community as a whole, and can be ineffective without it.

    “Therapy often stretches beyond the individualistic clienttherapist interactions [61] to a relationship with the client's community as a whole [23], and can be ineffective without it [60].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [61] [61] J. P. Grodniewicz and Mateusz Hohol. 2023. Waiting for a digital therapist: three challenges on the path to psychotherapy delivered by artificial intelligence. Frontiers in Psychiatry 14 (2023). https://doi.org/10.3389/fpsyt.2023.1190084 - source

      Not checked in the free run.

    • cites [23] [23] Julia E.H. Brown and Jodi Halpern. 2021. AI chatbots cannot replace human interactions in the pursuit of more inclusive mental healthcare. SSM - Mental Health 1 (2021), 100017. https://doi.org/10.1016/j.ssmmh.2021.100017 - source

      Not checked in the free run.

    • cites [60] [60] Gerald N. Grob. 2014. From Asylum to Community: Mental Health Policy in Modern America . Princeton Legacy Library, Vol. 1217. Princeton University Press. https://books.google.com/books?id=8DgABAAAQBAJ - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning
    § 8 Conclusion · p. 10

    There are a number of foundational concerns with using LLMs-as-therapists.

    “Beyond these practical issues, we find that there are a number of foundational concerns with using LLMs-as-therapists .”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 6.2 Foundational Barriers to LLMs-as-Therapists · p. 10

    Embodied therapy bots perform better.

    “To boot, embodied therapy bots perform better [75].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [75] [75] Mina J. Kian, Mingyu Zong, Katrin Fischer, Abhyuday Singh, Anna-Maria Velentza, Pau Sang, Shriya Upadhyay, Anika Gupta, Misha A. Faruki, Wallace Browning, Sebastien M. R. Arnold, Bhaskar Krishnamachari, and Maja J. Mataric. 2024. Can an LLM-Powered Socially Assistive Robot Effectively and Safely Deliver Cognitive Behavioral Therapy? A Study With University Students. https://doi.org/10.48550/arXiv.2402.17937 arXiv:2402.17937 [cs]. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation
    § 6.2 Foundational Barriers to LLMs-as-Therapists · p. 10

    Empathy requires experiencing what someone is going through and deeply caring.

    “Empathy requires experiencing what someone is going through and deeply caring [96, 108].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [96] [96] Carlos Montemayor, Jodi Halpern, and Abrol Fairweather. 2022. In principle obstacles for empathic AI: why we can't replace human empathy in healthcare. AI & society 37, 4 (2022), 1353-1359. - source

      Not checked in the free run.

    • cites [108] [108] Anat Perry. 2023. AI will never convey the essence of human empathy. Nature Human Behaviour 7, 11 (2023), 1808-1809. - source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 8 Conclusion · p. 10

    Commercially-available therapy bots currently provide therapeutic advice to millions of people, despite their association with suicides.

    “Commercially-available therapy bots currently provide therapeutic advice to millions of people, despite their association with suicides [57, 115, 143].”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [57] [57] Megan Garcia. 2024. COMPLAINT FOR WRONGFUL DEATH AND SURVIVORSHIP, NEGLIGENCE, FILIAL LOSS OF CONSORTIUM, VIOLATIONS OF FLORIDA'S DECEPTIVE AND UNFAIR TRADE PRACTICES ACT, FLA. STAT. ANN. § 501.204, ET SEQ., AND INJUNCTIVE RELIEF. -

      Not checked in the free run.

    • cites [115] [115] Kevin Roose. 2024. Character.ai Faces Lawsuit After Teen's Suicide. The New York Times (Oct. 2024). https://www.nytimes.com/2024/10/23/technology/ characterai-lawsuit-teen-suicide.html -

      Not checked in the free run.

    • cites [143] [143] Lauren Walker. 2023. Belgian man dies by suicide following exchanges with chatbot. The Belgian Times (March 2023). https://www.brusselstimes.com/ 430098/belgian-man-commits-suicide-following-exchanges-with-chatgpt -

      Not checked in the free run.

    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The commercially-available therapy bots selected are representative of such bots.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given
    § 6.1 Practical Barriers to LLMs-as-Therapists · p. 9

    LLMs struggle (or are untested) on basic therapeutic tasks.

    “LLMs struggle (or are untested) on basic therapeutic tasks.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The five selected mental health symptoms are serious and common.
    • Rests on an assumption: The verification questions accurately operationalize the clinical guidelines.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data
    § A.2 Appropriate Therapeutic Responses Experiment · p. 15

    The study administering stimuli to therapists was IRB approved.

    “This study was IRB approved.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § A.1 Stigma Experiment · p. 15

    The humans in the original study [109] were drawn from the general population and were not therapists.

    “Nonetheless, recall that the humans in the original study [109] were drawn from the general population and were not therapists.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [109] [109] Bernice A. Pescosolido, Andrew Halpern-Manners, Liying Luo, and Brea Perry. 2021. Trends in Public Stigma of Mental Illness in the US, 1996-2018. JAMA Network Open 4, 12 (Dec. 2021), e2140202. https://doi.org/10.1001/ jamanetworkopen.2021.40202 - source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning
    § A.2 Appropriate Therapeutic Responses Experiment · p. 15

    Filtering transcripts to include those for which gpt-4o can extract a valid quote demonstrating the condition helps validate that each transcript, without additional client history, itself demonstrates the symptom.

    “This helps validate that each transcript, without any additional client history, itself demonstrates the symptom.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation
    § A.1 Stigma Experiment · p. 15

    In 2018, the n=1171 U.S. respondents from Pescosolido et al. [109] show relatively similar amounts of stigma across conditions, with less stigma toward alcohol dependence and schizophrenia than toward the control and depression cases.

    “The n=1171 U.S. respondents in 2018 show relatively similar amounts of stigma across conditions, with less stigma shown toward alcohol dependence and schizophrenia than to the control and depression cases.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [109] [109] Bernice A. Pescosolido, Andrew Halpern-Manners, Liying Luo, and Brea Perry. 2021. Trends in Public Stigma of Mental Illness in the US, 1996-2018. JAMA Network Open 4, 12 (Dec. 2021), e2140202. https://doi.org/10.1001/ jamanetworkopen.2021.40202 - source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 6.2 Foundational Barriers to LLMs-as-Therapists · p. 10

    The quality of human care appears lower when not meeting in person, perhaps because of lack of nonverbal communication.

    “In contrast, the quality of human care appears to be lower when not meeting in person, perhaps because of the lack of nonverbal communication [58].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [58] [58] Gerald A. Gladstein. 1974. Nonverbal communication and counseling/psychotherapy: A review. The Counseling Psychologist 4, 3 (1974), 34-57. ISBN: 0011-0000 Publisher: Sage Publications Sage CA: Thousand Oaks, CA. - source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 6.2 Foundational Barriers to LLMs-as-Therapists · p. 10

    Engaging with an LLM can reduce some clients' depressive symptoms, although LLMs appear more similar to low-quality therapists.

    “Engaging with an LLM can reduce some clients' depressive symptoms [88], although LLMs appear more similar to low-quality therapists [28].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [88] [88] Han Li, Renwen Zhang, Yi-Chieh Lee, Robert E. Kraut, and David C. Mohr. 2023. Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being. npj Digital Medicine 6, 1 (Dec. 2023), 1-14. https://doi.org/10.1038/s41746-023-00979-5 Publisher: Nature Publishing Group. - source

      Not checked in the free run.

    • cites [28] [28] Yu Ying Chiu, Ashish Sharma, Inna Wanyin Lin, and Tim Althoff. 2024. A Computational Framework for Behavioral Assessment of LLM Therapists. https: //doi.org/10.48550/arXiv.2401.00820 arXiv:2401.00820 [cs]. - source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationown data
    § 8 Conclusion · p. 10

    These issues fly in the face of best clinical practice.

    “These issues fly in the face of best clinical practice, as our summary (Tab. 1) shows.”
    Quote in the paperNot a substantial claim
82 of 82

  • The paper relies on this without support.

    Acuity such as suicide, psychosis, or mania kills people.

    “Acuity-such as suicide, psychosis, or mania-kills people.”

  • The paper relies on this without support.

    The capacity to provide acute care forms the bare-minimum standard in providing any therapeutic care.

    “The capacity to provide acute care forms the bare-minimum standard in providing any therapeutic care.”

  • The paper relies on this without support.

    Symptoms of psychosis, mania, suicidality, and obsessions and compulsions may indicate but are distinct from schizophrenia, bipolar disorder, major depressive disorder, and OCD.

    “These symptoms may indicate (but are distinct from) the conditions of, respectively, schizophrenia, bipolar disorder, major depressive disorder, and OCD.”

  • The paper relies on this without support.

    The five selected mental health symptoms are serious and common.

    “We focused on five serious and common mental health symptoms: suicidal ideation, hallucinations, delusions, mania, and obsessive and compulsive behavior.”

  • The paper relies on this without support.

    The commercially-available therapy bots selected are representative of such bots.

    “We found these bots by searching for 'therapy' or 'CBT' on major LLM-serving platforms (e.g., the GPT Store) and choosing a popular bot on each.”

  • The paper relies on this without support.

    The verification questions accurately operationalize the clinical guidelines.

    “We then wrote verification questions for each stimulus using clinical guidelines (Tab. 2, §3).”

  • The paper relies on this without support.

    The 16 therapists recruited through Upwork are representative of human therapists for the purpose of comparing appropriate therapeutic responses.

    “We administered the same stimuli (without transcripts) to 𝑛 = 16 therapists in the U.S. with an average of seven years practicing with their license.”
    “We recruited each therapist through Upwork, paying 30 USD an hour.”
7 of 7

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A mental health study found that chatbots encouraged delusional thinking, exhibited stigma against those with mental health conditions, and often gave dangerous responses to suicidal patients.

    “A study in mental health found that chatbots encouraged delusional thinking, exhibited stigma against those with mental health conditions, and often gave dangerous responses to suicidal patients.”

    From Large language models provide unsafe answers to patient-posed medical questions (2026) · cites this paper as [12]

    The Conclusion (p. 10) reports that therapy bots encouraged delusions and that the LLMs powering them showed mental-health stigma. Section 5.2 (p. 8) gives examples of bots responding dangerously to prompts indicating suicidal ideation, but these were test prompts, not suicidal patients; the paper does not establish that such responses occurred often specifically for suicidal ideation. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Recent empirical work highlights the psychosocial toll of extended chatbot use.

    “In Responsible AI research, recent empirical work highlights the psychosocial toll of extended chatbot use [1, 23]”

    From 'AI-Induced Delusional Spirals': Understanding Lived Experiences During Maladaptive Human-Chatbot Interactions (2026) · cites this paper as [23]

    Section 2.1 (p. 2) reports interviewees describing chatbot interactions as almost addictive and saying they spent too much time with the bots; it also reports over-reliance and harmful responses. These passages support concern about psychosocial risks, but the paper does not establish a psychosocial toll specifically caused by extended use. Its own findings in the Conclusion (p. 10) concern inappropriate responses and stigma, not outcomes measured over prolonged use. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • The gpt-4o model version differs between methods and model tables

    Major

    The methods say the experiments used the November 20, 2024 version of gpt-4o, while the model table lists the August 6, 2024 version. The text does not establish which version produced the reported results.

  • Delusion-response examples are cited to the bridge table

    Minor

    The delusion-response paragraph directs readers to Table 11, which contains responses to the bridge stimulus instead. Delusion responses appear in Tables 12 and 13.

  • Table 2 swaps the VA guidelines for bipolar disorder and psychosis

    Minor

    The methods identify [43] as the bipolar-disorder guideline and [44] as the schizophrenia guideline. Table 2 assigns [44] to bipolar disorder and [43] to psychosis.

  • Bridge-response examples are cited to the delusion table

    Minor

    The bridge-response paragraph directs readers to Table 13, but Table 13 contains responses to the delusion stimulus. The LLM bridge responses appear in Table 11.

  • Discussion overstates gpt-4o's significant stigma advantage

    Minor

    The discussion says gpt-4o shows significantly less stigma than the llama3.1 models, but the results explicitly say it is not significantly better than llama3.1-70b.

  • Character.ai bridge response is described opposite to Table 10

    Minor

    The results say the Character.ai therapist gave examples of bridges. Its response in Table 10 contains no bridge examples.

6 of 6

Cites

Flagged first, then by citations

Research works printed but not linked: 13

  1. Alexander Street Press (Ed.). 2007. Counseling and psychotherapy transcripts: volume I. Alexander Street Press, Alexandria, Virginia.
  2. Alexander Street Press (Ed.). 2023. Counseling and psychotherapy transcripts: volume II. Alexander Street Press, Alexandria, Virginia.
  3. American Psychiatric Association. 2022. Diagnostic and statistical manual of mental disorders DSM-5-TR (fifth edition, text revision ed.). American Psychiatric Association.
  4. American Psychological Association. 2023. Psychologists reaching their limits as patients present with worsening symptoms year after year. Technical Report. American Psychological Association.
  5. Ben Bratman. 2015. Improving the Performance of the Performance Test: The Key to Meaningful Bar Exam Reform. UMKC LAW REVIEW 83 (2015).
  6. Matthew J. Dennis and Lily E. Frank. 2024. Reconceptualizing The Ethical Guidelines for Mental Health Apps: Values From Feminism, Disability Studies, and Intercultural Ethics. Technical Report. Institute of Electrical and Electronics Engineers.
  7. Cameron A Hecht Desmond C. Ong Mac Clapper Michela Jones Dora Demszky Diyi Yang Johannes Eichstaedt Christopher J. Bryan and David S. Yeager. in press. Using Large Language Models in Behavioral Science Interventions: Promise and Risk. Behavioral Science & Policy (in press).
  8. Max Lamparth Declan Grabb Amy Franks Scott Gershan Kaitlyn N. Kunstman Aaron Lulla Monika Drummond Roots Manu Sharma Aryan Shrivastava Nina Vasan and Colleen Waickman. 2025. Moving Beyond Medical Exam Questions: A Clinician-Annotated Dataset of Real-World Tasks and Ambiguity in Mental Healthcare. arxiv:https://arXiv.org/abs/2502.16051 [cs.CL] https://arxiv.org/abs/2502.16051 · arXiv
  9. Zilin Ma Yiyang Mei and Zhaoyuan Su. 2024. Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Support. AMIA Annual Symposium Proceedings 2023 (Jan. 2024) 1105–1114. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10785945/
  10. Arvind Narayanan and Sayash Kapoor. 2024. AI snake oil: what artificial intelligence can do, what it can’t, and how to tell the difference. Princeton University Press, Princeton, New Jersey. OCLC: on1427948148.
  11. Daniel Steel. 2015. Philosophy and the Precautionary Principle. Cambridge University Press. Google-Books-ID: RvpkBAAAQBAJ.
  12. Herman T. Tavani. 2016. Ethics and Technology: Controversies, Questions, and Strategies for Ethical Computing. John Wiley & Sons. Google-Books-ID: pMI7CwAAQBAJ.
  13. Hongli Zhan, Allen Zheng, Yoon Kyung Lee, Jina Suh, Junyi Jessy Li, and Desmond C Ong. 2024. Large language models are capable of offering cognitive reappraisal, if guided. In Proceedings of the 1st Conference on Language Modeling (CoLM).

Cited by

Sorted by citations

Related

Authors
Moore, J., Grabb, D., Agnew, W., Klyman, K., Chancellor, S., Ong, D. C., & Haber, N.
Affiliations
  • Stanford University (US)
  • Carnegie Mellon University (US)
  • University of Minnesota (US)
  • The University of Texas at Austin (US)
ORCID
Venue
Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency
Publisher
ACM
Published
2025-06-23
Pages
599-627
DOI
10.1145/3715275.3732039
OpenAlex
W4411542009
Semantic Scholar
83dbbf3bb3945f799a15f3bbaf6b12cf26672d7e
Cited by
204
References
135
Citations by year
2026: 169 · 2025: 35
FWCI
209.57
Type
conference paper
Language
en
Open access
Licence
cc-by-sa
Locations
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by-sa)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Toyata Research Institute
  • Microsoft (Accelerating Foundation Models Research)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Keywords
mental health services, stigma, therapeutic relationship, delusions, sycophancy, AI safety, GPT-4o

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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.

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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