Research graph
References from Effectiveness of Chatbots in Mental Health Screening and Assessment: Systematic Review. Local targets link to admitted publications; unresolved targets remain external evidence.
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Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019
10.1016/s2215-0366(21)00395-3 · External reference
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The Lancet Commission on ending stigma and discrimination in mental health
10.1016/s0140-6736(22)01470-2 · External reference
Transforming mental health systems globally: principles and policy recommendations
10.1016/s0140-6736(23)00918-2 · External reference
The applications of large language models in mental health: scoping review
10.2196/69284 · External reference
Comparing GPT-4 and human researchers in health care data analysis: qualitative description study
10.2196/56500 · External reference
Conversational agents in the treatment of mental health problems: mixed-method systematic review
10.2196/14166 · External reference
Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis
10.2196/16021 · External reference
Evaluating diagnostic accuracy and treatment efficacy in mental health: a comparative analysis of large language model tools and mental health professionals
10.3390/ejihpe15010009 · External reference
Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes
10.1016/j.ebiom.2024.105401 · External reference
Artificially intelligent chatbots in digital mental health interventions: a review
10.1080/17434440.2021.2013200 · External reference
Chatbots and mental health: a scoping review of reviews
10.1007/s12144-025-08094-2 · External reference
Charting the evolution of artificial intelligence mental health chatbots from rule-based systems to large language models: a systematic review
10.1002/wps.21352 · External reference
The now and future of ChatGPT and GPT in psychiatry
10.1111/pcn.13588 · External reference
Assessing the accuracy and reliability of large language models in psychiatry using standardized multiple-choice questions: cross-sectional study
10.2196/69910 · External reference
Evaluation of large language models on mental health: from knowledge test to illness diagnosis
10.3389/fpsyt.2025.1646974 · External reference
The effectiveness of AI chatbots in alleviating mental distress and promoting health behaviors among adolescents and young adults: systematic review and meta-analysis
10.2196/79850 · External reference
Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained large language models
10.1007/s10489-024-05808-0 · External reference
Competency of large language models in evaluating appropriate responses to suicidal ideation: comparative study
10.2196/67891 · External reference
The impact of history of depression and access to weapons on suicide risk assessment: a comparison of ChatGPT-3.5 and ChatGPT-4
10.7717/peerj.17468 · External reference
Quantified language connectedness in schizophrenia-spectrum disorders
10.1016/j.psychres.2021.114130 · External reference
Assessing dimensions of thought disorder with large language models: the tradeoff of accuracy and consistency
10.1016/j.psychres.2024.116119 · External reference
AI and narrative embeddings detect PTSD following childbirth via birth stories
10.1038/s41598-024-54242-2 · External reference
Beyond human expertise: the promise and limitations of ChatGPT in suicide risk assessment
10.3389/fpsyt.2023.1213141 · External reference
Large language models versus expert clinicians in crisis prediction among telemental health patients: comparative study
10.2196/58129 · External reference
A call to action on assessing and mitigating bias in artificial intelligence applications for mental health
10.1177/17456916221134490 · External reference
Regulating AI in mental health: ethics of care perspective
10.2196/58493 · External reference
Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data
10.1038/s44184-024-00057-y · External reference
Artificial Intelligence in mental health and the biases of language based models
10.1371/journal.pone.0240376 · External reference
Deconstructing demographic bias in speech-based machine learning models for digital health
10.3389/fdgth.2024.1351637 · External reference
Racial and ethnic differences in depression: current perspectives
10.2147/ndt.s128584 · External reference
Vickybot, a chatbot for anxiety-depressive symptoms and work-related burnout in primary care and health care professionals: development, feasibility, and potential effectiveness studies
10.2196/43293 · External reference
Psychometric properties of a chatbot version of the PHQ-9 with adults and older adults
10.3389/fdgth.2021.645805 · External reference
Physician versus large language model chatbot responses to web-based questions from autistic patients in Chinese: cross-sectional comparative analysis
10.2196/54706 · External reference
Language sentiment predicts changes in depressive symptoms
10.1073/pnas.2321321121 · External reference
Development and evaluation of a mental health chatbot using ChatGPT 4.0: mixed methods user experience study with Korean users
10.2196/63538 · External reference
Early detection of depression using a conversational AI bot: a non-clinical trial
10.1371/journal.pone.0279743 · External reference
Mental distress, label avoidance, and use of a mental health chatbot: results from a US survey
10.2196/45959 · External reference
Large language models and text embeddings for detecting depression and suicide in patient narratives
10.1001/jamanetworkopen.2025.11922 · External reference
Evaluating the agreement between ChatGPT-4 and validated questionnaires in screening for anxiety and depression in college students: a cross-sectional study
10.1186/s12888-025-06798-0 · External reference
GPT-4 shows potential for identifying social anxiety from clinical interview data
10.1038/s41598-024-82192-2 · External reference
Using large language models to detect depression from user-generated diary text data as a novel approach in digital mental health screening: instrument validation study
10.2196/54617 · External reference
The effect of perceived burdensomeness and thwarted belongingness on therapists’ assessment of patients’ suicide risk
10.1080/10503307.2015.1013161 · External reference
Aligning large language models for enhancing psychiatric interviews through symptom delineation and summarization: pilot study
10.2196/58418 · External reference
Integrative diagnosis of psychiatric conditions using ChatGPT and fMRI data
10.1186/s12888-025-06586-w · External reference
Assessing the acceptability of a sleep-targeted digital intervention among geriatric inpatients: a preliminary study
10.1177/20552076241293935 · External reference
Transparency and the black box problem: why we do not trust AI
10.1007/s13347-021-00477-0 · External reference
