ArticleWorld psychiatry : official journal of the World Psychiatric Association (WPA)2025
Charting the evolution of artificial intelligence mental health chatbots from rule-based systems to large language models: a systematic review.
Article in World psychiatry : official journal of the World Psychiatric Association (WPA), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
What it found
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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
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Patterns of Uptake, Engagement, and Attrition in Randomized Controlled Trials of Digital Interventions for Eating Disorders: A Systematic Review and Meta-Analysis.The International journal of eating disorders · 2026Pooled it
- Testing and evaluation of generative large language models in electronic health record applications: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- The Impact of Chatbot Type and Normative Messaging on Chatbot Usage Intention Based on the Health Technology Acceptance Model: Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- Tool or Companion? Reframing Conversational AI to Prevent Psychological Harm.JMIR mental health · 2026Review
- Exploring Real-World Use of AI Chatbots for Mental Health Support: Cross-Sectional Survey Study.JMIR mental health · 2026Article
- Benchmarking Generative Artificial Intelligence Against Human Judgment in Eating Disorder Case Recognition and Treatment Recommendations.The International journal of eating disorders · 2026Article
- Leveraging expectation effects to improve outcomes in the context of digital mental health interventions.Internet interventions · 2026Review
- From Personalization to Therapeutic Continuity: Framework for Memory in AI-Powered Mental Health Systems.JMIR AI · 2026Article
- A scoping review on the mental health harms of LLM-based chatbots.NPJ digital medicine · 2026Article
- Review
- An Acceptance Criteria Framework for Determining the Implementation Fit of Custom Large Language Models in Public Health Interventions.Journal of medical Internet research · 2026Article
- AI Agents Are Coming: 5-Stage Taxonomy of Language-Based AI Systems for Psychiatry, Psychotherapy, and Counseling.JMIR mental health · 2026Article
- Digital Mental Health Research Priorities, Revisited for the AI and Large Language Model Era.JMIR mental health · 2026Article
- Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026Article
- Human 2.0? AI and the Future of Well-Being, Connection, and Personal Growth: A Narrative Review.Behavioral sciences (Basel, Switzerland) · 2026Review
- Mapping Practice-Based Signals of Generative AI in Psychiatric Care: Qualitative Study of Korean Psychiatrists' Experiences, Interpretations, and Implementation Priorities.Journal of medical Internet research · 2026Article
- A framework for clinical validation of generative artificial intelligence therapeutics.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Exploring Student Perceptions of Generative AI Therapists and Their Benefits and Challenges in Schools.International journal of psychology : Journal international de psychologie · 2026Article
- Efficacy of AI-delivered cognitive behavioral therapy interventions for anxiety and depressive symptoms: a systematic review.NPJ digital medicine · 2026Article
- Barriers and Facilitators to the Use of Large Language Model-Based Conversational Agents in Mental Healthcare: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid evolution of artificial intelligence (AI) chatbots in mental health care presents a fragmented landscape with variable clinical evidence and evaluation rigor. This systematic review of 160 studies (2020-2024) classifies chatbot architectures - rule-based, machine learning-based, and large language model (LLM)-based - and proposes a three-tier evaluation framework: foundational bench testing (technical validation), pilot feasibility testing (user engagement), and clinical efficacy testing (symptom reduction). While rule-based systems dominated until 2023, LLM-based chatbots surged to 45% of new studies in 2024. However, only 16% of LLM studies underwent clinical efficacy testing, with most (77%) still in early validation. Overall, only 47% of studies focused on clinical efficacy testing, exposing a critical gap in robust validation of therapeutic benefit. Discrepancies emerged between marketed claims ("AI-powered") and actual AI architectures, with many interventions relying on simple rule-based scripts. LLM-based chatbots are increasingly studied for emotional support and psychoeducation, yet they pose unique ethical concerns, including incorrect responses, privacy risks, and unverified therapeutic effects. Despite their generative capabilities, LLMs remain largely untested in high-stakes mental health contexts. This paper emphasizes the need for standardized evaluation and benchmarking aligned with medical AI certification to ensure safe, transparent and ethical deployment. The proposed framework enables clearer distinctions between technical novelty and clinical efficacy, offering clinicians, researchers and regulators ordered steps to guide future standards and benchmarks. To ensure that AI chatbots enhance mental health care, future research must prioritize rigorous clinical efficacy trials, transparent architecture reporting, and evaluations that reflect real-world impact rather than the well-known potential.
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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.