The DOI matches its Crossref record; 49 of the 50 references it cites were found in a registry or an index.
Your robot therapist is not your therapist: understanding the role of AI-powered mental health chatbots
Open accessAbstract
Artificial intelligence (AI)-powered chatbots have the potential to substantially increase access to affordable and effective mental health services by supplementing the work of clinicians. Their 24/7 availability and accessibility through a mobile phone allow individuals to obtain help whenever and wherever needed, overcoming financial and logistical barriers. Although psychological AI chatbots have the ability to make significant improvements in providing mental health care services, they do not come without ethical and technical challenges. Some major concerns include providing inadequate or harmful support, exploiting vulnerable populations, and potentially producing discriminatory advice due to algorithmic bias. However, it is not always obvious for users to fully understand the nature of the relationship they have with chatbots. There can be significant misunderstandings about the exact purpose of the chatbot, particularly in terms of care expectations, ability to adapt to the particularities of users and responsiveness in terms of the needs and resources/treatments that can be offered. Hence, it is imperative that users are aware of the limited therapeutic relationship they can enjoy when interacting with mental health chatbots. Ignorance or misunderstanding of such limitations or of the role of psychological AI chatbots may lead to a therapeutic misconception (TM) where the user would underestimate the restrictions of such technologies and overestimate their ability to provide actual therapeutic support and guidance. TM raises major ethical concerns that can exacerbate one's mental health contributing to the global mental health crisis. This paper will explore the various ways in which TM can occur particularly through inaccurate marketing of these chatbots, forming a digital therapeutic alliance with them, receiving harmful advice due to bias in the design and algorithm, and the chatbots inability to foster autonomy with patients.
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“This risk can occur from the underrepresentation of severe mental health cases in training data, training on stigmatizing data, or by LLM-CBs reflecting users’ biases41.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [41]
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“As general-purpose LLMs are not medical applications, entered data is not protected with the standards typical for medical data, and data protection often fails HIPAA standards41,48.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [41]
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“Mentioned by two articles, therapeutic misconception may arise when LLM-CB behaviors (e.g., simulated empathy, validation) lead to users overestimating its abilities to provide psychotherapeutic support while underestimating its limitations41.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [41]
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Metadata
- Authors
- Khawaja, Z., & Bélisle-Pipon, J.-C.
- Affiliations
- Simon Fraser University (CA)
- ORCID
- Khawaja, Zoha
- Venue
- Frontiers in Digital Health
- Publisher
- Frontiers Media SA
- Published
- 2023-11-08
- Volume
- 5
- Pages
- 1278186-1278186
- ISSN
- 2673-253X (ISSN-L)
- PMC
- PMC10663264
- OpenAlex
- W4388524014
- Cited by
- 214
- References
- 50
- Citations by year
- 2026: 95 · 2025: 87 · 2024: 32
- FWCI
- 55.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- mental health chatbots, therapeutic misconception, artificial intelligence in mental health care, algorithmic bias, chatbot marketing, patient autonomy, vulnerable populations, access to mental health care
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