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Current Psychiatry Reports

Erschienen in:

30.11.2023

An Introduction to Generative Artificial Intelligence in Mental Health Care: Considerations and Guidance

verfasst von: Darlene R. King, Guransh Nanda, Joel Stoddard, Allison Dempsey, Sarah Hergert, Jay H. Shore, John Torous

Erschienen in: Current Psychiatry Reports | Ausgabe 12/2023

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Abstract

Purpose of Review

This paper provides an overview of generative artificial intelligence (AI) and the possible implications in the delivery of mental health care.

Recent Findings

Generative AI is a powerful technology that is changing rapidly. As psychiatrists, it is important for us to understand generative AI technology and how it may impact our patients and our practice of medicine.

Summary

This paper aims to build this understanding by focusing on GPT-4 and its potential impact on mental health care delivery. We first introduce key concepts and terminology describing how the technology works and various novel uses of it. We then dive into key considerations for GPT-4 and other large language models (LLMs) and wrap up with suggested future directions and initial guidance to the field.

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Sam Altman: CEO of OpenAI calls for US to regulate artificial intelligence. BBC News [Internet]. 2023 May 16 [cited 2023 May 19]; Available from: https://www.bbc.com/news/world-us-canada-65616866.

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• What is chatGPT doing … and why does it work?. 2023 [cited 2023 Apr 25]. Available from: https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/. This article summarizes how LLMs such as ChatGPT function in the context of language processing, machine learning, and neural nets. It intuitively explains how LLMs are trained and function, with a focus on ChatGPT.

21.

• Zhao WX, Zhou K, Li J, Tang T, Wang X, Hou Y, et al. A survey of large language models. arXiv; 2023 [cited 2023 Apr 30]. Available from: http://arxiv.org/abs/2303.18223. This article is a technical view of the recent developments made with LLMs and focuses on pre-training, adaptation tuning, utilization, and capacity evaluation of these models. It is a thorough resource about the current state of LLMs.

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Nadeem R. 60% of Americans would be uncomfortable with provider relying on AI in their own health care. Pew Research Center Science & Society. 2023 [cited 2023 May 14]. Available from: https://www.pewresearch.org/science/2023/02/22/60-of-americans-would-be-uncomfortable-with-provider-relying-on-ai-in-their-own-health-care/.

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• Lagan S, Aquino P, Emerson MR, Fortuna K, Walker R, Torous J. Actionable health app evaluation: translating expert frameworks into objective metrics. NPJ Digit Med. 2020;3(1):1–8. This paper is an excellent review of the APA App Evaluation Model. It details the process by which the model was created discussing the available models at the time, the deficits that existed, and how the APA model was synthesized to address those deficits and provide an objective metric for app evaluation. CrossRef

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• Nissenbaum H. Privacy as contextual integrity. Wash Law Rev. 2004;79. This paper details the importance of privacy and elaborates on privacy policy in the US. “Contextual Integrity” is explained as a concept that ties privacy to the norms of a particular context. HIPAA is discussed as a privacy law that specially recognizes health and medical information.

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• Connolly SL. Leveraging implementation science to understand factors influencing sustained use of mental health apps: a narrative review’. J Technol Behav Sci. 2020;1–13. This review discusses the implementation of mental health apps including the qualities that lead to the success and use of such apps. The article highlights various factors such as simplicity, benefits over current tools, ease of navigation, and alignment with user’s needs and skill sets that facilitate mental health app implementation.

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King D. ChatGPT not yet ready for clinical practice. Psychiatr News [Internet]. 2023 Jun 16 [cited 2023 Jul 24]; Available from: https://psychnews.psychiatryonline.org/doi/10.1176/appi.pn.2023.07.7.56.

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Reardon S. Scientific American. [cited 2023 Jul 24]. AI chatbots could help provide therapy, but caution is needed. Available from: https://www.scientificamerican.com/article/ai-chatbots-could-help-provide-therapy-but-caution-is-needed/.

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• Cooper AF, Moss E, Laufer B, Nissenbaum H. Accountability in an Algorithmic Society: relationality, responsibility, and robustness in machine learning. In: 2022 ACM conference on fairness, accountability, and transparency. 2022 [cited 2023 May 5]. p. 864–76. Available from: http://arxiv.org/abs/2202.05338. This paper discusses the important topic of accountability in relation to ML and AI. With data driven algorithmic processes such as these being used or being introduced to a variety of fields including medicine, barriers to accountability with their use become apparent. Overall, the paper introduces how the lack of accountability for errors or mistakes of AI poses a challenge in its implementation in healthcare.

Titel: An Introduction to Generative Artificial Intelligence in Mental Health Care: Considerations and Guidance
verfasst von: Darlene R. King
Guransh Nanda
Joel Stoddard
Allison Dempsey
Sarah Hergert
Jay H. Shore
John Torous
Publikationsdatum: 30.11.2023
Verlag: Springer US
Erschienen in: Current Psychiatry Reports / Ausgabe 12/2023
Print ISSN: 1523-3812
Elektronische ISSN: 1535-1645
DOI: https://doi.org/10.1007/s11920-023-01477-x

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Springer Medizin

An Introduction to Generative Artificial Intelligence in Mental Health Care: Considerations and Guidance

Abstract

Purpose of Review

Recent Findings

Summary

Neu im Fachgebiet Psychiatrie

Darf man die Behandlung eines Neonazis ablehnen?

Wartezeit nicht kürzer, aber Arbeit flexibler

Chirurginnen und Chirurgen sind stark suizidgefährdet

Ein Drittel der jungen Ärztinnen und Ärzte erwägt abzuwandern

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Klimawandel und Gesundheit: Was Sie in der Hausarztpraxis wissen müssen und tun können

Springer Medizin

Abstract

Purpose of Review

Recent Findings

Summary

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