ArticleFamily medicine and community health2023
Identifying depression and its determinants upon initiating treatment: ChatGPT versus primary care physicians.
Article in Family medicine and community health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 5 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.
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Who cites it
46 citing papers in PubMed, 5 syntheses or guidelines pooled it, 86 citations in OpenAlex.
- Leveraging chatbots for enhanced decision-making: a comprehensive literature review.Frontiers in artificial intelligence · 2026Pooled it
- "It's Not Only Attention We Need": Systematic Review of Large Language Models in Mental Health Care.JMIR mental health · 2025Pooled it
- The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review.JMIR mental health · 2025Pooled it
- Large Language Models for Chatbot Health Advice Studies: A Systematic Review.JAMA network open · 2025Pooled it
- Applications of large language models in psychiatry: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Healthcare consumption among patients with stress-related exhaustion: a register-based study in Swedish primary care.Scandinavian journal of primary health care · 2026Article
- How do Artificial Intelligence chatbots respond to questions from adolescent personas about their eating, body weight or appearance?Child and adolescent 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
- Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis.JMIR AI · 2026Review
- Conversational AI should fill the white space in mental health care, not replace humans.NPJ digital medicine · 2026Review
- AI-Based Diagnostic Platform Capabilities With Lyme Disease as a Use Case: Integrative Exploration.Online journal of public health informatics · 2026Article
- Large Language Models and Their Applications in Mental Health: Scoping Review.JMIR mental health · 2026Article
- Exploring the potential of ChatGPT as a digital advisor in acute psychiatric crises: a feasibility study.Der Nervenarzt · 2026Article
- Using AI to Train Future Clinicians in Depression Assessment: Feasibility Study.JMIR medical education · 2026Article
- Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions.Journal of general internal medicine · 2026Article
- Digital Therapeutics and Blended Care in Major Depressive Disorder: Current Evidence and Future Directions.Advances in experimental medicine and biology · 2026Review
- Dignity in mental health care: the conceptual connections between sense of dignity, stigma and self-stigma among people with mental disorder diagnoses.Frontiers in psychiatry · 2026Article
- ChatGPT Clinical Use in Mental Health Care: Scoping Review of Empirical Evidence.JMIR mental health · 2025Article
- Large Language Models for Cardiovascular Disease, Cancer, and Mental Disorders: A Review of Systematic Reviews.Healthcare (Basel, Switzerland) · 2025Review
- "HIV Stigma Exists" - Exploring ChatGPT's HIV Advice by Race and Ethnicity, Sexual Orientation, and Gender Identity.Journal of racial and ethnic health disparities · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo compare evaluations of depressive episodes and suggested treatment protocols generated by Chat Generative Pretrained Transformer (ChatGPT)-3 and ChatGPT-4 with the recommendations of primary care physicians.
methodsVignettes were input to the ChatGPT interface. These vignettes focused primarily on hypothetical patients with symptoms of depression during initial consultations. The creators of these vignettes meticulously designed eight distinct versions in which they systematically varied patient attributes (sex, socioeconomic status (blue collar worker or white collar worker) and depression severity (mild or severe)). Each variant was subsequently introduced into ChatGPT-3.5 and ChatGPT-4. Each vignette was repeated 10 times to ensure consistency and reliability of the ChatGPT responses.
resultsFor mild depression, ChatGPT-3.5 and ChatGPT-4 recommended psychotherapy in 95.0% and 97.5% of cases, respectively. Primary care physicians, however, recommended psychotherapy in only 4.3% of cases. For severe cases, ChatGPT favoured an approach that combined psychotherapy, while primary care physicians recommended a combined approach. The pharmacological recommendations of ChatGPT-3.5 and ChatGPT-4 showed a preference for exclusive use of antidepressants (74% and 68%, respectively), in contrast with primary care physicians, who typically recommended a mix of antidepressants and anxiolytics/hypnotics (67.4%). Unlike primary care physicians, ChatGPT showed no gender or socioeconomic biases in its recommendations.
conclusionChatGPT-3.5 and ChatGPT-4 aligned well with accepted guidelines for managing mild and severe depression, without showing the gender or socioeconomic biases observed among primary care physicians. Despite the suggested potential benefit of using atificial intelligence (AI) chatbots like ChatGPT to enhance clinical decision making, further research is needed to refine AI recommendations for severe cases and to consider potential risks and ethical issues.
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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.