This paper is in the following e-collection/theme issue:

Research Letter (38) Artificial Intelligence (1409) Perinatal Depression; Postpartum Depression; PPD (95) Chatbots and Conversational Agents (542) Generative Language Models Including ChatGPT (320) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1552)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49240, first published .
Clinical Accuracy of Large Language Models and Google Search Responses to Postpartum Depression Questions: Cross-Sectional Study

Clinical Accuracy of Large Language Models and Google Search Responses to Postpartum Depression Questions: Cross-Sectional Study

Clinical Accuracy of Large Language Models and Google Search Responses to Postpartum Depression Questions: Cross-Sectional Study

Authors of this article:

Emre Sezgin1, 2 Author Orcid Image ;   Faraaz Chekeni1, 2 Author Orcid Image ;   Jennifer Lee1, 2 Author Orcid Image ;   Sarah Keim1, 2, 3 Author Orcid Image
Article Authors Cited by (15) Tweetations (13) Metrics

Journals

  1. Suárez A, Jiménez J, Llorente de Pedro M, Andreu-Vázquez C, Díaz-Flores García V, Gómez Sánchez M, Freire Y. Beyond the Scalpel: Assessing ChatGPT's potential as an auxiliary intelligent virtual assistant in oral surgery. Computational and Structural Biotechnology Journal 2024;24:46 View
  2. Sezgin E. Redefining Virtual Assistants in Health Care: The Future With Large Language Models. Journal of Medical Internet Research 2024;26:e53225 View
  3. Sallam M, Barakat M, Sallam M. A Preliminary Checklist (METRICS) to Standardize the Design and Reporting of Studies on Generative Artificial Intelligence–Based Models in Health Care Education and Practice: Development Study Involving a Literature Review. Interactive Journal of Medical Research 2024;13:e54704 View
  4. García-Méndez S, de Arriba-Pérez F. Large Language Models and Healthcare Alliance: Potential and Challenges of Two Representative Use Cases. Annals of Biomedical Engineering 2024;52(8):1928 View
  5. Wang L, Ma Y, Bi W, Lv H, Li Y. An Entity Extraction Pipeline for Medical Text Records Using Large Language Models: Analytical Study. Journal of Medical Internet Research 2024;26:e54580 View
  6. Dagli M, Oettl F, Gujral J, Malhotra K, Ghenbot Y, Yoon J, Ozturk A, Welch W. Clinical Accuracy, Relevance, Clarity, and Emotional Sensitivity of Large Language Models to Surgical Patient Questions: Cross-Sectional Study. JMIR Formative Research 2024;8:e56165 View
  7. Sezgin E, McKay I. Behavioral health and generative AI: a perspective on future of therapies and patient care. npj Mental Health Research 2024;3(1) View
  8. Omar M, Soffer S, Charney A, Landi I, Nadkarni G, Klang E. Applications of large language models in psychiatry: a systematic review. Frontiers in Psychiatry 2024;15 View
  9. Lawrence H, Schneider R, Rubin S, Matarić M, McDuff D, Jones Bell M. The Opportunities and Risks of Large Language Models in Mental Health. JMIR Mental Health 2024;11:e59479 View
  10. Guo Z, Lai A, Thygesen J, Farrington J, Keen T, Li K. Large Language Models for Mental Health Applications: Systematic Review. JMIR Mental Health 2024;11:e57400 View
  11. Taniguchi M, Lindsey J. Performance of chatbots in queries concerning fundamental concepts in photochemistry. Photochemistry and Photobiology 2024 View
  12. Kolding S, Lundin R, Hansen L, Østergaard S. Use of generative artificial intelligence (AI) in psychiatry and mental health care: a systematic review. Acta Neuropsychiatrica 2024:1 View
  13. Denecke K, Gabarron E. The ethical aspects of integrating sentiment and emotion analysis in chatbots for depression intervention. Frontiers in Psychiatry 2024;15 View

Books/Policy Documents

  1. da Silva G, Ulbricht V. Artificial Intelligence for Supporting Human Cognition and Exploratory Learning in the Digital Age. View