ArticleFamily medicine and community health2024
Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public.
Article in Family medicine and community health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 7 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 7 syntheses or guidelines pooled 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
- Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Use of generative artificial intelligence (AI) in psychiatry and mental health care: a systematic review.Acta neuropsychiatrica · 2024Pooled it
- Large Language Models for Mental Health Applications: Systematic Review.JMIR mental health · 2024Pooled it
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Applications of large language models in psychiatry: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Large language models and conditional rules in clinical decision support systems.Health information science and systems · 2026Article
- Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis.JMIR AI · 2026Review
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- 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
- Breaking bad news with reflective AI virtual experience (BRAVE): development and evaluation of a generative AI-based simulation tool.Advances in simulation (London, England) · 2026Article
- Understanding psychiatrist readiness for AI: a study of access, self-efficacy, trust, and design expectations.BMC health services research · 2026Article
- Large Language Models and Primary Care: A Scoping Review.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 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
- Identifying psychiatric manifestations in outpatients with depression and anxiety: a large language model-based approach.Npj mental health research · 2025Article
- Enhanced large language models for effective screening of depression and anxiety.Communications medicine · 2025Article
- Feasibility of Using ChatGPT to Generate Exposure Hierarchies for Treating Obsessive-Compulsive Disorder.Behavior therapy · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has rapidly permeated various sectors, including healthcare, highlighting its potential to facilitate mental health assessments. This study explores the underexplored domain of AI's role in evaluating prognosis and long-term outcomes in depressive disorders, offering insights into how AI large language models (LLMs) compare with human perspectives.
methodsUsing case vignettes, we conducted a comparative analysis involving different LLMs (ChatGPT-3.5, ChatGPT-4, Claude and Bard), mental health professionals (general practitioners, psychiatrists, clinical psychologists and mental health nurses), and the general public that reported previously. We evaluate the LLMs ability to generate prognosis, anticipated outcomes with and without professional intervention, and envisioned long-term positive and negative consequences for individuals with depression.
resultsIn most of the examined cases, the four LLMs consistently identified depression as the primary diagnosis and recommended a combined treatment of psychotherapy and antidepressant medication. ChatGPT-3.5 exhibited a significantly pessimistic prognosis distinct from other LLMs, professionals and the public. ChatGPT-4, Claude and Bard aligned closely with mental health professionals and the general public perspectives, all of whom anticipated no improvement or worsening without professional help. Regarding long-term outcomes, ChatGPT 3.5, Claude and Bard consistently projected significantly fewer negative long-term consequences of treatment than ChatGPT-4.
conclusionsThis study underscores the potential of AI to complement the expertise of mental health professionals and promote a collaborative paradigm in mental healthcare. The observation that three of the four LLMs closely mirrored the anticipations of mental health experts in scenarios involving treatment underscores the technology's prospective value in offering professional clinical forecasts. The pessimistic outlook presented by ChatGPT 3.5 is concerning, as it could potentially diminish patients' drive to initiate or continue depression therapy. In summary, although LLMs show potential in enhancing healthcare services, their utilisation requires thorough verification and a seamless integration with human judgement and skills.
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