The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis
Open accessAbstract
Large language model (LLM)–powered services are gaining popularity in various applications due to their exceptional performance in many tasks, such as sentiment analysis and answering questions. Recently, research has been exploring their potential use in digital health contexts, particularly in the mental health domain. However, implementing LLM-enhanced conversational artificial intelligence (CAI) presents significant ethical, technical, and clinical challenges. In this viewpoint paper, we discuss 2 challenges that affect the use of LLM-enhanced CAI for individuals with mental health issues, focusing on the use case of patients with depression: the tendency to humanize LLM-enhanced CAI and their lack of contextualized robustness. Our approach is interdisciplinary, relying on considerations from philosophy, psychology, and computer science. We argue that the humanization of LLM-enhanced CAI hinges on the reflection of what it means to simulate “human-like” features with LLMs and what role these systems should play in interactions with humans. Further, ensuring the contextualization of the robustness of LLMs requires considering the specificities of language production in individuals with depression, as well as its evolution over time. Finally, we provide a series of recommendations to foster the responsible design and deployment of LLM-enhanced CAI for the therapeutic support of individuals with depression.
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Individuals with mental health issues may misinterpret LLM-CBs as empathetic and caring, leading to unrealistic expectations of their supportive capabilities.
“Furthermore, individuals with mental health issues may misinterpret LLM-CBs as empathetic and caring, leading to unrealistic expectations of their supportive capabilities36.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [36]
In “Humanizing LLM-Enhanced CAI: a Philosophical and Psychological Perspective,” the paper says vulnerable patients with depression may mistake CAI for empathetic and caring, leading to unrealistic expectations of warmth and acceptance. The citing sentence extends that concern to individuals with mental health issues generally, a broader population than the passage addresses. (LLM, conf=0.85)
Experimental — accuracy not yet measuredData security concerns stem from security risks relating to sensitive health data provided by users or by clinicians.
“Data security concerns (mentioned by 13 articles) stem from security risks relating to sensitive health data provided by users or by clinicians36–39,44,47,50,51,55.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [36]
In “Contextualizing the Robustness of LLMs” (page unavailable), the paper warns that nonrobust models can be tricked into revealing personal information. “LLMs in Health Care and Mental Health Applications” describes processing patients’ responses and mental-health histories. These passages support concern about sensitive patient data, but do not specifically identify data provided by clinicians as a source of that risk. (LLM, conf=0.83)
Experimental — accuracy not yet measuredProtective guardrails on LLM-CBs can be bypassed (jailbreaking) to enable generation of otherwise restricted content, including harmful advice like guides on self-harm.
“Although LLM-CBs are often refined by protective guardrails, those can be bypassed (i.e., “jailbreaking”) to enable the generation of otherwise restricted content, including harmful advice like guides on self-harm36.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [36]
LLM-CBs may, in certain situations (e.g., via jailbreaking), generate inappropriate or harmful content, such as guides to self-harm.
“As discussed above and mentioned by nine articles, LLM-CBs may, in certain situations (e.g., via jailbreaking), generate inappropriate or harmful content, such as guides to self-harm36.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [36]
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Influential
- Authors
- Ferrario, A., Sedlakova, J., & Trachsel, M.
- Affiliations
- University of Zurich (CH)
- University of Basel (CH)
- Corresponding author
- Ferrario, Andrea
- Venue
- JMIR Mental Health
- Publisher
- JMIR Publications Inc.
- Published
- 2024-07-02
- Volume
- 11
- Pages
- e56569-e56569
- ISSN
- 2368-7959 (ISSN-L)
- PMC
- PMC11231450
- OpenAlex
- W4395689022
- Cited by
- 61
- References
- 34
- Citations by year
- 2026: 36 · 2025: 19 · 2024: 6
- FWCI
- 24.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Mental Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection
- Zurich Open Repository and Archive (University of Zurich)
- 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
- large language models, conversational AI, depression, humanization of AI, digital mental health, responsible AI design, therapeutic support
- MeSH terms
- Artificial Intelligence, Communication, Depression, Humans, Humanism, Language
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 ›
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