ArticleJournal of multidisciplinary healthcare2024
Assessing the Effectiveness of ChatGPT in Delivering Mental Health Support: A Qualitative Study.
Article in Journal of multidisciplinary healthcare, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 6 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review.Journal of medical Internet research · 2026Pooled it
- The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review.JMIR mental health · 2025Pooled it
- Evaluating Generative AI in Mental Health: Systematic Review of Capabilities and Limitations.JMIR mental health · 2025Pooled it
- Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review.Journal of medical Internet research · 2025Pooled it
- Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways.Frontiers in public health · 2025Pooled it
- Use of generative artificial intelligence (AI) in psychiatry and mental health care: a systematic review.Acta neuropsychiatrica · 2024Pooled it
- Are You Qualified, ChatGPT? Examining Clinical Skills and Competencies of ChatGPT in Delivering Systemic Interventions.Journal of marital and family therapy · 2026Article
- Exploring Real-World Use of AI Chatbots for Mental Health Support: Cross-Sectional Survey Study.JMIR mental health · 2026Article
- A scoping review on the mental health harms of LLM-based chatbots.NPJ digital medicine · 2026Article
- Real-world use of large language models for mental health in 2024.NPJ digital medicine · 2026Article
- The Associations of Emotional Intelligence, AI Self-Efficacy, and AI Literacy Among Nursing Undergraduates Under the NUR.S.E.S. Framework: Network Analysis.JMIR nursing · 2026Article
- Network and machine learning analysis of childhood trauma, mental health, and AI-based emotional support needs in adolescents from underdeveloped regions.BMC psychology · 2026Article
- Barriers and Facilitators to the Use of Large Language Model-Based Conversational Agents in Mental Healthcare: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Exploring the potential of ChatGPT as a digital advisor in acute psychiatric crises: a feasibility study.Der Nervenarzt · 2026Article
- Codesigning a Nurse-Led, Large Language Model-Empowered Agent to Increase Hepatitis B Screening and Vaccination for Inclusion Health Populations: A Research Protocol.Nursing reports (Pavia, Italy) · 2026Article
- Mapping applications and evaluations of LLM-enabled AI chatbots for health purposes: a scoping review.Frontiers in public health · 2026Article
- Assessing large language model responses to pediatric depression FAQs: a cross-sectional study on readability, accuracy, and sentiment.Frontiers in psychiatry · 2026Article
- Disclosure is not destigmatization: rethinking AI and mental health stigma.Frontiers in public health · 2026Article
- Public attitudes and practices toward using AI chatbots for healthcare assistance: a multinational cross-sectional study.BMC health services research · 2025Article
- ChatGPT Clinical Use in Mental Health Care: Scoping Review of Empirical Evidence.JMIR mental health · 2025Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial Intelligence (AI) applications are widely researched for their potential in effectively improving the healthcare operations and disease management. However, the research trend shows that these applications also have significant negative implications on the service delivery. Purpose: To assess the use of ChatGPT for mental health support. Methods: Due to the novelty and unfamiliarity of the ChatGPT technology, a quasi-experimental design was chosen for this study. Outpatients from a public hospital were included in the sample. A two-week experiment followed by semi-structured interviews was conducted in which participants used ChatGPT for mental health support. Semi-structured interviews were conducted with 24 individuals with mental health conditions. Results: Eight positive factors (psychoeducation, emotional support, goal setting and motivation, referral and resource information, self-assessment and monitoring, cognitive behavioral therapy, crisis interventions, and psychotherapeutic exercises) and four negative factors (ethical and legal considerations, accuracy and reliability, limited assessment capabilities, and cultural and linguistic considerations) were associated with the use of ChatGPT for mental health support. Conclusion: It is important to carefully consider the ethical, reliability, accuracy, and legal challenges and develop appropriate strategies to mitigate them in order to ensure safe and effective use of AI-based applications like ChatGPT in mental health support.
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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.