ArticleDigital health
Large language models outperform general practitioners in identifying complex cases of childhood anxiety.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07199231 (A Comparative Performance Evaluation of Four Publicly Available Large Language Models Against Gold Standard Medical References), which is not on this map. Cited by 5 papers.
What it found
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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.
A Comparative Performance Evaluation of Four Publicly Available Large Language Models Against Gold Standard Medical References
Who cites it
5 citing papers in PubMed.
- Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis.JMIR AI · 2026Review
- Assessing large language model responses to pediatric depression FAQs: a cross-sectional study on readability, accuracy, and sentiment.Frontiers in psychiatry · 2026Article
- ChatGPT Clinical Use in Mental Health Care: Scoping Review of Empirical Evidence.JMIR mental health · 2025Article
- Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis.Journal of medical Internet research · 2025Article
- Evaluating Diagnostic Accuracy and Treatment Efficacy in Mental Health: A Comparative Analysis of Large Language Model Tools and Mental Health Professionals.European journal of investigation in health, psychology and education · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Anxiety is prevalent in childhood but often remains undiagnosed due to its physical manifestations and significant comorbidity. Despite the availability of effective treatments, including medication and psychotherapy, research indicates that physicians struggle to identify childhood anxiety, particularly in complex and challenging cases. This study aims to explore the potential effectiveness of artificial intelligence (AI) language models in diagnosing childhood anxiety compared to general practitioners (GPs). Methods: During February 2024, we evaluated the ability of several large language models (LLMs; ChatGPT-3.5 and ChatGPT-4, Claude.AI, Gemini) to identify cases childhood anxiety disorder, compared with reports of GPs. Results: AI tools exhibited significantly higher rates of identifying anxiety than GPs. Each AI tool accurately identified anxiety in at least one case: Claude.AI and Gemini identified at least four cases, ChatGPT-3 identified three cases, and ChatGPT-4 identified one or two cases. Additionally, 40% of GPs preferred to manage the cases within their practice, often with the help of a practice nurse, whereas AI tools generally recommended referral to specialized mental or somatic health services. Conclusion: Preliminary findings indicate that LLMs, specifically Claude.AI and Gemini, exhibit notable diagnostic capabilities in identifying child anxiety, demonstrating a comparative advantage over GPs.
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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.