Large language models outperform general practitioners in identifying complex cases of childhood anxiety - Inbar Levkovich; Eyal Rabin; Michal Brann; Zohar Elyoseph (Digital Health)
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.