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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Artificial intelligence: Augmenting telehealth with large language models.

Centaine L Snoswell^1,2, Aaron J Snoswell³, Jaimon T Kelly^1,2

¹Centre for Online Health, The University of Queensland, Brisbane, Australia.

Journal of Telemedicine and Telecare

|April 12, 2023

Summary

This summary is machine-generated.

Large language models (LLMs) are a disruptive AI technology set to change how patients and clinicians access information. Telehealth professionals must understand LLM capabilities and limitations for effective integration.

Keywords:

ChatGPT Machine learning artificial intelligence digital health large language models telehealth telemedicine

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Area of Science:

Artificial Intelligence
Natural Language Processing

Background:

Emerging large language models (LLMs) represent a significant advancement in machine learning.
LLMs, exemplified by ChatGPT, are poised to become integral to major search engines and software platforms.

Discussion:

The integration of LLMs into search engines like Bing and Google, and Microsoft products, will fundamentally alter information access for patients and clinicians.
This technological shift necessitates awareness among telehealth professionals regarding the functionalities and constraints of LLMs.

Key Insights:

LLMs are a transformative technology impacting healthcare information dissemination.
Understanding LLM capabilities and limitations is crucial for telehealth practitioners.

Outlook:

Future integration of LLMs promises to reshape patient-clinician interactions and information retrieval.
Telehealth clinicians need to proactively adapt to and leverage LLM technology.