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ChatGPT and mental healthcare: balancing benefits with risks of harms
  1. Charlotte Blease1,2,
  2. John Torous3
  1. 1 Participatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala Universitet, Uppsala, Sweden
  2. 2 Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
  3. 3 Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
  1. Correspondence to Dr Charlotte Blease, Participatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala Universitet, Uppsala, Sweden; charlotte.blease{at}uu.se

Abstract

Against the global need for increased access to mental services, health organisations are looking to technological advances to improve the delivery of care and lower costs. Since November 2022, with the public launch of OpenAI’s ChatGPT, the field of generative artificial intelligence (AI) has received expanding attention. Although generative AI itself is not new, technical advances and the increased accessibility of large language models (LLMs) (eg, OpenAI’s GPT-4 and Google’s Bard) suggest use of these tools could be clinically significant. LLMs are an application of generative AI technology that can summarise and generate content based on training on vast data sets. Unlike search engines, which provide internet links in response to typed entries, chatbots that rely on generative language models can simulate dialogue that resembles human conversations. We examine the potential promise and the risks of using LLMs in mental healthcare today, focusing on their scope to impact mental healthcare, including global equity in the delivery of care. Although we caution that LLMs should not be used to disintermediate mental health clinicians, we signal how—if carefully implemented—in the long term these tools could reap benefits for patients and health professionals.

  • Machine Learning
  • Depression & mood disorders
  • Adult psychiatry
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Twitter @crblease, @JohnTorousMD

  • Contributors CB wrote the first draft. JT and CB revised the paper until both signed off on it.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.