Artificial intelligence (AI) in psychotherapy: A challenging frontier
Open accessAbstract
Objective: Artificial intelligence (AI) chatbots have emerged as a potential tool to revolutionise mental health care, offering innovative solutions for the diagnosis and management of psychiatric conditions. AI psychotherapy is being trialled as a possible replacement or adjunct to traditional human-led therapy, showing promise in enhancing the accessibility and personalisation of mental health care. This paper seeks to explore the potential risks of AI for psychotherapy. Conclusions: AI psychotherapy represents relatively unchartered territory. There are concerns surrounding the trainability of AI chatbots, as well as the ultimate ability for an AI to effectively deliver human-like care. We must also consider other consequences, such as the potential for technological misuse. Thus, as AI continues to evolve, we must approach its integration with caution, and ensure the necessary regulatory mechanisms are in place for its effective and equitable implementation.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Biases can occur via underrepresentation of certain groups in training data, training data including stigmatizing representations, and sycophantic reflection of users' expressed biases.
“Specifically, biases can occur via underrepresentation of certain groups in training data, training data including stigmatizing representations, and sycophantic reflection of users’ expressed biases50,51.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [51]
The ‘Algorithmic biases and training’ section says that training primarily on male demographic data can minimize women’s experiences and perpetuate stereotypes, supporting the underrepresentation point. It does not say that the training data themselves contain stigmatizing representations. The ‘Wisdom and empathy’ section describes unconditional positive regard and difficulty offering criticism, but does not specifically say AI reflects users’ expressed biases. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe lack of empathy in LLM-CBs may be recognized by users, limiting a sense of connection and security.
“The lack of empathy may be recognized by users, limiting a sense of connection and security51.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [51]
The “Wisdom and empathy” section says AI is unlikely to replicate a human therapist’s empathy and that patients value the compassion that establishes connection and security. It does not say that users recognize AI’s lack of empathy, so that part of the claim is not supported. No page number is available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Charlson, F., van Ommeren, M., Flaxman, A., Cornett, J., Whiteford, H., & Saxena, S. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30934-1 · Cited by 1,702 · see citing works
Fiske, A., Henningsen, P., & Buyx, A. (2019) · Journal of Medical Internet Research · doi:10.2196/13216 · Cited by 857 · see citing works
Liu, H., Peng, H., Song, X., Xu, C., & Zhang, M. (2022) · Internet Interventions · doi:10.1016/j.invent.2022.100495 · Cited by 296 · cited 1× · see citing works
“8,9 For example, most AI algorithms are trained primarily on male demographic data, resulting in a lack of sensitivity to issues that women face, leading to the minimisation of experiences and even the perpetuation of maladaptive stereotypes.”
Artificial intelligence and chatbots in psychiatry
Pham, K. T., Nabizadeh, A., & Selek, S. (2022) · Psychiatric Quarterly · doi:10.1007/s11126-022-09973-8 · Cited by 241 · see citing works
Warmerdam, L., Smit, F., van Straten, A., Riper, H., & Cuijpers, P. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1436 · Cited by 158 · cited 1× · see citing works
“19,20 However, this introduces the potential for an oversaturated online market, with various products that drastically differ in price and quality.”
Generative artificial intelligence in mental health care: potential benefits and current challenges
Torous, J., & Blease, C. (2024) · World Psychiatry · doi:10.1002/wps.21148 · Cited by 90 · see citing works
King, D. R., Nanda, G., Stoddard, J., Dempsey, A., Hergert, S., Shore, J. H., & Torous, J. (2023) · Current Psychiatry Reports · doi:10.1007/s11920-023-01477-x · Cited by 63 · see citing works
Flagged first, then by citations
Research works printed but not linked: 14
- [1]Beg MJ Verma M Vckm M, et al. Artificial intelligence for psychotherapy: a review of the current state and future directions. Indian J Psychol Med 2024. PMC12359021 40831472
- [4]Sun J Dong QX Wang SW, et al. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr 2023; 87: 103705. 37506575
- [5]Mucci F Marazziti D. Artificial intelligence in neuropsychiatry: a potential beacon in an ocean of uncertainty? Clin Neuropsychiatry 2023; 20: 467– 471. 38344464 PMC10852407
- [6]Olawade DB Wada OZ Odetayo A, et al. Enhancing mental health with Artificial Intelligence: current trends and future prospects. J Med Surg Public Health 2024; 3: 100099.
- [7]Bond RR Mulvenna MD Potts C, et al. Digital transformation of mental health services. Npj Ment Health Res 2023; 2: 13. 38609479 PMC10955947
- [8]Brown JEH Halpern J. AI chatbots cannot replace human interactions in the pursuit of more inclusive mental healthcare. SSM - Mental Health 2021; 1: 100017.
- [10]Currie G John G Hewis J. Gender and ethnicity bias in generative artificial intelligence text-to-image depiction of pharmacists. Int J Pharm Pract 2024; 32: 524– 531. 39228085
- [11]Chan WW Fitzsimmons-Craft EE Smith AC, et al. The challenges in designing a prevention chatbot for eating disorders: observational study. JMIR Form Res 2022; 6: e28003. 35044314 PMC8811687
- [13]Lee EE Torous J De Choudhury M, et al. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging 2021; 6: 856– 864. 33571718 PMC8349367
- [15]Liu J ChatGPT: perspectives from human–computer interaction and psychology. Front Artif Intell 2024; 7: 1418869. 38957452 PMC11217544
- [16]Cheng SW Chang CW Chang WJ, et al. The now and future of ChatGPT and GPT in psychiatry. Psychiatry Clin Neurosci 2023; 77: 592– 596. 37612880 PMC10952959
- [17]Dwyer DB Falkai P Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol 2018; 14: 91– 118. 29401044
- [18]Ray A Bhardwaj A Malik YK, et al. Artificial intelligence and Psychiatry: an overview. Asian J Psychiatr 2022; 70: 103021. 35219978 PMC9760544
- [20]Richards D. Artificial intelligence and psychotherapy: a counterpoint. Couns Psychother Res 2025; 25: e12758.
Cited by
Sorted by citations
A scoping review on the mental health harms of LLM-based chatbots
Diel, A., Torous, J., Cuijpers, P., Kleesiek, J., Nensa, F., Weber, N., Faust, F., Lalgi, T. J., Mellis, F. S., Teufel, M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-03054-x · Cited by 4 · see citing works
“The lack of empathy may be recognized by users, limiting a sense of connection and security 51 .”
- Authors
- Jesudason, D., Bacchi, S., & Bastiampillai, T.
- Affiliations
- The University of Adelaide (AU)
- Harvard University (US)
- Flinders University (AU)
- Corresponding author
- Jesudason, Daniel
- Venue
- Australasian Psychiatry
- Publisher
- SAGE Publications
- Published
- 2025-05-27
- Volume
- 33
- Issue
- 4
- Pages
- 629-632
- ISSN
- 1039-8562 (ISSN-L)
- 1440-1665
- PMC
- PMC12314210
- OpenAlex
- W4410794437
- Cited by
- 12
- References
- 7
- Citations by year
- 2026: 10 · 2025: 2
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Australasian Psychiatry (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- University of Adelaide
- Kinghorn Foundation
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence, AI chatbots, mental health services, psychiatric diagnosis, access to mental health care, individualized care, AI regulation, equity in mental health services
- MeSH terms
- Artificial Intelligence, Mental Disorders, Humans, Psychotherapy
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
