ReviewFrontiers in artificial intelligence2023
A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement.
Review in Frontiers in artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- A bibliometric analysis of large language models in mental health research.Frontiers in psychiatry · 2026Pooled it
- Applications of large language models in psychiatry: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Are You Qualified, ChatGPT? Examining Clinical Skills and Competencies of ChatGPT in Delivering Systemic Interventions.Journal of marital and family therapy · 2026Article
- Frameworks, Methodologies, and Tools for Evaluating Large Language Models in Digital Mental Health Interventions: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- A scoping review on the mental health harms of LLM-based chatbots.NPJ digital medicine · 2026Article
- Article
- Urgent considerations for suicide prevention in the safe and ethical use of artificial intelligence.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2026Article
- Delusional Experiences Emerging From AI Chatbot Interactions or "AI Psychosis".JMIR mental health · 2025Review
- Exploring the Role of Artificial Intelligence in Smart Healthcare: A Capability and Function-Oriented Review.Healthcare (Basel, Switzerland) · 2025Review
- Applying language models for suicide prevention: evaluating news article adherence to WHO reporting guidelines.Npj mental health research · 2025Article
- AI Chatbots for Psychological Health for Health Professionals: Scoping Review.JMIR human factors · 2025Article
- Conversational AI in Pediatric Mental Health: A Narrative Review.Children (Basel, Switzerland) · 2025Review
- Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review.JMIR mental health · 2025Article
- Think FAST: a novel framework to evaluate fidelity, accuracy, safety, and tone in conversational AI health coach dialogues.Frontiers in digital health · 2025Article
- From robots to chatbots: unveiling the dynamics of human-AI interaction.Frontiers in psychology · 2025Review
- Generative AI in consumer health: leveraging large language models for health literacy and clinical safety with a digital health framework.Frontiers in digital health · 2025Review
- Mental Health Applications of Generative AI and Large Language Modeling in the United States.International journal of environmental research and public health · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Virtual Mental Health Assistants (VMHAs) continuously evolve to support the overloaded global healthcare system, which receives approximately 60 million primary care visits and 6 million emergency room visits annually. These systems, developed by clinical psychologists, psychiatrists, and AI researchers, are designed to aid in Cognitive Behavioral Therapy (CBT). The main focus of VMHAs is to provide relevant information to mental health professionals (MHPs) and engage in meaningful conversations to support individuals with mental health conditions. However, certain gaps prevent VMHAs from fully delivering on their promise during active communications. One of the gaps is their inability to explain their decisions to patients and MHPs, making conversations less trustworthy. Additionally, VMHAs can be vulnerable in providing unsafe responses to patient queries, further undermining their reliability. In this review, we assess the current state of VMHAs on the grounds of user-level explainability and safety, a set of desired properties for the broader adoption of VMHAs. This includes the examination of ChatGPT, a conversation agent developed on AI-driven models: GPT3.5 and GPT-4, that has been proposed for use in providing mental health services. By harnessing the collaborative and impactful contributions of AI, natural language processing, and the mental health professionals (MHPs) community, the review identifies opportunities for technological progress in VMHAs to ensure their capabilities include explainable and safe behaviors. It also emphasizes the importance of measures to guarantee that these advancements align with the promise of fostering trustworthy conversations.
Indexed as
Identifiers
What OpenQuestion holds
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.