Large language models outperform general practitioners in identifying complex cases of childhood anxiety.

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 signific

SUBMITTER: Levkovich I 

PROVIDER: S-EPMC11648044 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature