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. 2022 Feb 10;10(2):e32875.
doi: 10.2196/32875.

Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model

Emre Sezgin #  1 , Joseph Sirrianni #  1 , Simon L Linwood  2
Affiliations

Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model

Emre Sezgin et al. JMIR Med Inform. .

Abstract

Generative pretrained transformer models have been popular recently due to their enhanced capabilities and performance. In contrast to many existing artificial intelligence models, generative pretrained transformer models can perform with very limited training data. Generative pretrained transformer 3 (GPT-3) is one of the latest releases in this pipeline, demonstrating human-like logical and intellectual responses to prompts. Some examples include writing essays, answering complex questions, matching pronouns to their nouns, and conducting sentiment analyses. However, questions remain with regard to its implementation in health care, specifically in terms of operationalization and its use in clinical practice and research. In this viewpoint paper, we briefly introduce GPT-3 and its capabilities and outline considerations for its implementation and operationalization in clinical practice through a use case. The implementation considerations include (1) processing needs and information systems infrastructure, (2) operating costs, (3) model biases, and (4) evaluation metrics. In addition, we outline the following three major operational factors that drive the adoption of GPT-3 in the US health care system: (1) ensuring Health Insurance Portability and Accountability Act compliance, (2) building trust with health care providers, and (3) establishing broader access to the GPT-3 tools. This viewpoint can inform health care practitioners, developers, clinicians, and decision makers toward understanding the use of the powerful artificial intelligence tools integrated into hospital systems and health care.

Keywords: artificial intelligence; chatbot; clinical informatics; generative pretrained transformer; natural language processing.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
GPT-3 use case (chatbot triaging and patient note summarization). API: application programming interface; EHR: electronic health record; GPT-3: generative pretrained transformer 3.
Figure 2
Figure 2
A model of operational and implementation considerations for generative pretrained transformer 3. IS: information systems.
See this image and copyright information in PMC

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References

    1. Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler DM, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D. Language models are few-shot learners. arXiv. Preprint posted online on July 22, 2020. https://arxiv.org/pdf/2005.14165.pdf
    1. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. 31st Conference on Neural Information Processing Systems (NIPS 2017); December 4-9, 2017; Long Beach, California, USA. 2017. http://papers.nips.cc/paper/7181-attention-is-all-you-%0Aneed.pdf
    1. Hutson M. Robo-writers: the rise and risks of language-generating AI. Nature. 2021 Mar;591(7848):22–25. doi: 10.1038/d41586-021-00530-0.10.1038/d41586-021-00530-0 - DOI - PubMed
    1. Liu J, Shen D, Zhang Y, Dolan B, Carin L, Chen W. What makes good in-context examples for GPT-3? arXiv. Preprint posted online on January 17, 2021. https://arxiv.org/pdf/2101.06804.pdf
    1. Emerson. GPT-3 Demo. [2021-12-14]. https://gpt3demo.com/apps/quickchat-emerson .

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