Artificial intelligence: Augmenting telehealth with large language models

Centaine L. Snoswell; Aaron J. Snoswell; Jaimon T. Kelly; Liam J. Caffery; Anthony C. Smith

doi:10.1177/1357633X231169055

Artificial intelligence: Augmenting telehealth with large language models

Centaine L. Snoswell^*
, Aaron J. Snoswell
, Jaimon T. Kelly
, Liam J. Caffery
, Anthony C. Smith

^*Corresponding author for this work

Research output: Contribution to journal › Article › Research › peer-review

17 Citations (Scopus)

Abstract

This brief editorial describes an emerging area of machine learning technology called large language models (LLMs). LLMs, such as ChatGPT, are the technological disruptor of this decade. They are going to be integrated into search engines (Bing and Google) and into Microsoft products in the coming months. They will therefore fundamentally change the way patients and clinicians access and receive information. It is essential that telehealth clinicians are aware of LLMs and appreciate their capabilities and limitations.

Original language	English
Pages (from-to)	150-154
Number of pages	5
Journal	Journal of Telemedicine and Telecare
Volume	31
Issue number	1
DOIs	https://doi.org/10.1177/1357633X231169055
Publication status	Published - Jan 2025
Externally published	Yes

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10.1177/1357633X231169055

Cite this

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title = "Artificial intelligence: Augmenting telehealth with large language models",

abstract = "This brief editorial describes an emerging area of machine learning technology called large language models (LLMs). LLMs, such as ChatGPT, are the technological disruptor of this decade. They are going to be integrated into search engines (Bing and Google) and into Microsoft products in the coming months. They will therefore fundamentally change the way patients and clinicians access and receive information. It is essential that telehealth clinicians are aware of LLMs and appreciate their capabilities and limitations.",

author = "Snoswell, \{Centaine L.\} and Snoswell, \{Aaron J.\} and Kelly, \{Jaimon T.\} and Caffery, \{Liam J.\} and Smith, \{Anthony C.\}",

note = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dr C L Snoswell was funded by the UQ retention fellowship. Dr J Kelly was supported by a Postdoctoral Fellowship from the National Heart Foundation of Australia during this project (106081). Dr A J Snoswell is funded by the Australian Research Council Centre of Excellence for Automated Decision-Making and Society (CE200100005). Publisher Copyright: {\textcopyright} The Author(s) 2023.",

year = "2025",

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AU - Smith, Anthony C.

N1 - Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dr C L Snoswell was funded by the UQ retention fellowship. Dr J Kelly was supported by a Postdoctoral Fellowship from the National Heart Foundation of Australia during this project (106081). Dr A J Snoswell is funded by the Australian Research Council Centre of Excellence for Automated Decision-Making and Society (CE200100005). Publisher Copyright: © The Author(s) 2023.

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N2 - This brief editorial describes an emerging area of machine learning technology called large language models (LLMs). LLMs, such as ChatGPT, are the technological disruptor of this decade. They are going to be integrated into search engines (Bing and Google) and into Microsoft products in the coming months. They will therefore fundamentally change the way patients and clinicians access and receive information. It is essential that telehealth clinicians are aware of LLMs and appreciate their capabilities and limitations.

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Artificial intelligence: Augmenting telehealth with large language models

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