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Mental Health Applications of Generative AI and Large Language Modeling in the United States

Author

Listed:
  • Sri Banerjee

    (School of Health Sciences and Public Policy, Walden University, Minneapolis, MN 55401, USA)

  • Pat Dunn

    (Center for Health Technology & Innovation American Heart Association, Dallas, TX 75231, USA)

  • Scott Conard

    (Converging Health, Irving, TX 75039, USA)

  • Asif Ali

    (McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA)

Abstract

(1) Background: Artificial intelligence (AI) has flourished in recent years. More specifically, generative AI has had broad applications in many disciplines. While mental illness is on the rise, AI has proven valuable in aiding the diagnosis and treatment of mental disorders. However, there is little to no research about precisely how much interest there is in AI technology. (2) Methods: We performed a Google Trends search for “AI and mental health” and compared relative search volume (RSV) indices of “AI”, “AI and Depression”, and “AI and anxiety”. This time series study employed Box–Jenkins time series modeling to forecast long-term interest through the end of 2024. (3) Results: Within the United States, AI interest steadily increased throughout 2023, with some anomalies due to media reporting. Through predictive models, we found that this trend is predicted to increase 114% through the end of the year 2024, with public interest in AI applications being on the rise. (4) Conclusions: According to our study, we found that the awareness of AI has drastically increased throughout 2023, especially in mental health. This demonstrates increasing public awareness of mental health and AI, making advocacy and education about AI technology of paramount importance.

Suggested Citation

  • Sri Banerjee & Pat Dunn & Scott Conard & Asif Ali, 2024. "Mental Health Applications of Generative AI and Large Language Modeling in the United States," IJERPH, MDPI, vol. 21(7), pages 1-12, July.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:7:p:910-:d:1433707
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    References listed on IDEAS

    as
    1. Ahmad Radwan & Mohannad Amarneh & Hussam Alawneh & Huthaifa I. Ashqar & Anas AlSobeh & Aws Abed Al Raheem Magableh, 2024. "Predictive Analytics in Mental Health Leveraging LLM Embeddings and Machine Learning Models for Social Media Analysis," International Journal of Web Services Research (IJWSR), IGI Global, vol. 21(1), pages 1-22, January.
    2. Nicholas J Carson & Brian Mullin & Maria Jose Sanchez & Frederick Lu & Kelly Yang & Michelle Menezes & Benjamin Lê Cook, 2019. "Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-14, February.
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