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Conference object . 2023
License: CC BY
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Article . 2023
License: CC BY
Data sources: Datacite
ZENODO
Article . 2023
License: CC BY
Data sources: Datacite
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Adaptive user interface framework powered by a large language model for culturally sensitive virtual healthcare applications

Authors: Ghosh, Akash; Yan, Yan; Lin, Wenjun;

Adaptive user interface framework powered by a large language model for culturally sensitive virtual healthcare applications

Abstract

In this research, we propose the development of anAdaptive User Interface (UI) Framework for virtual healthcareapplications, powered by a Large Language Model (LLM). Theintention is to revolutionize the way healthcare services arerendered by creating a real-time responsive system that catersto diverse patient needs. Unlike conventional healthcareapplications, this framework utilizes various sensors andinteractive inputs to continuously adapt to users' feedback. Itharnesses the potential of deep learning to process this feedbackand make culturally sensitive adaptations, ensuring morepersonalized and effective care for Indigenous, Black, andPeople of Colour (IBPOC) populations. A unique aspect of thissystem is that its adaptations are not predetermined; instead, itdynamically generates changes based on the user feedbackanalyzed by the LLM. To demonstrate the efficacy of thisframework, a demo healthcare application is being developed.We expect this initiative to significantly contribute to the field ofvirtual healthcare by introducing a more inclusive, personalized,and adaptive platform, ultimately leading to improved patientcare outcomes.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green