ArticleJMIR mental health2024
The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis.
Article in JMIR mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Therapeutic Interaction Features of AI Chatbots in Depression Interventions: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues.Journal of medical Internet research · 2025Pooled it
- "It's Not Only Attention We Need": Systematic Review of Large Language Models in Mental Health Care.JMIR mental health · 2025Pooled it
- Sentiment analysis in public health: a systematic review of the current state, challenges, and future directions.Frontiers in public health · 2025Pooled it
- Liability and Standard of Care in AI-Driven Psychiatric Practice: European Viewpoint.Journal of medical Internet research · 2026Article
- A scoping review on the mental health harms of LLM-based chatbots.NPJ digital medicine · 2026Article
- Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach.Artificial intelligence in medicine · 2026Article
- Large language models in clinical psychiatry: Applications and optimization strategies.World journal of psychiatry · 2025Review
- A Prompt Engineering Framework for Large Language Model-Based Mental Health Chatbots: Conceptual Framework.JMIR mental health · 2025Article
- Exploring the Application of AI and Extended Reality Technologies in Metaverse-Driven Mental Health Solutions: Scoping Review.Journal of medical Internet research · 2025Article
- Experience of Cardiovascular and Cerebrovascular Disease Surgery Patients: Sentiment Analysis Using the Korean Bidirectional Encoder Representations from Transformers (KoBERT) Model.JMIR medical informatics · 2025Article
- Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review.JMIR mental health · 2025Article
- Responsible Design, Integration, and Use of Generative AI in Mental Health.JMIR mental health · 2025Article
- The externalization of internal experiences in psychotherapy through generative artificial intelligence: a theoretical, clinical, and ethical analysis.Frontiers in digital health · 2025Article
- An Ethical Perspective on the Democratization of Mental Health With Generative AI.JMIR mental health · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.