This cross-sectional study surveyed US adolescents and young adults on their use of generative artificial intelligence (AI) for mental health advice, including frequency and perceived helpfulness.

Telehealth service utilization expanded rapidly at the COVID-19 pandemic outset, particularly for mental health conditions.¹ Unlike physical health conditions that may require physical examinations or laboratory testing, many mental health services can be provided virtually.² Three years after the 2020 SARS-CoV-2 national public health emergency (PHE) declaration, many facets of the US health care system have returned to normal.³ However, trends in mental health service utilization and spending before expiration of the PHE in May 2023 are largely undocumented. Therefore, we assessed monthly telehealth vs in-person utilization and spending rates for mental health services among commercially insured US adults between 2019 and 2022.

Background: With suicide rates in the United States at an all-time high, individuals experiencing suicidal ideation are increasingly turning to large language models (LLMs) for guidance and support. Objective: The objective of this study was to assess the competency of 3 widely used LLMs to distinguish appropriate versus inappropriate responses when engaging individuals who exhibit suicidal ideation. Methods: This observational, cross-sectional study evaluated responses to the revised Suicidal Ideation Response Inventory (SIRI-2) generated by ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro. Data collection and analyses were conducted in July 2024. A common training module for mental health professionals, SIRI-2 provides 24 hypothetical scenarios in which a patient exhibits depressive symptoms and suicidal ideation, followed by two clinician responses. Clinician responses were scored from –3 (highly inappropriate) to +3 (highly appropriate). All 3 LLMs were provided with a standardized set of instructions to rate clinician responses. We compared LLM responses to those of expert suicidologists, conducting linear regression analyses and converting LLM responses to z scores to identify outliers (z score>1.96 or <–1.96; P<0.05). Furthermore, we compared final SIRI-2 scores to those produced by health professionals in prior studies. Results: All 3 LLMs rated responses as more appropriate than ratings provided by expert suicidologists. The item-level mean difference was 0.86 for ChatGPT (95% CI 0.61-1.12; P<.001), 0.61 for Claude (95% CI 0.41-0.81; P<.001), and 0.73 for Gemini (95% CI 0.35-1.11; P<.001). In terms of z scores, 19% (9 of 48) of ChatGPT responses were outliers when compared to expert suicidologists. Similarly, 11% (5 of 48) of Claude responses were outliers compared to expert suicidologists. Additionally, 36% (17 of 48) of Gemini responses were outliers compared to expert suicidologists. ChatGPT produced a final SIRI-2 score of 45.7, roughly equivalent to master’s level counselors in prior studies. Claude produced an SIRI-2 score of 36.7, exceeding prior performance of mental health professionals after suicide intervention skills training. Gemini produced a final SIRI-2 score of 54.5, equivalent to untrained K-12 school staff. Conclusions: Current versions of 3 major LLMs demonstrated an upward bias in their evaluations of appropriate responses to suicidal ideation; however, 2 of the 3 models performed equivalent to or exceeded the performance of mental health professionals.

Despite estimates of rising prevalence of children with autism spectrum disorder (ASD) in the US, there is little research on the availability of behavioral health care services for this vulnerable...