Redefining Digital Health Interfaces with Large Language Models
- URL: http://arxiv.org/abs/2310.03560v3
- Date: Thu, 29 Feb 2024 18:37:40 GMT
- Title: Redefining Digital Health Interfaces with Large Language Models
- Authors: Fergus Imrie, Paulius Rauba, Mihaela van der Schaar
- Abstract summary: Large Language Models (LLMs) have emerged as general-purpose models with the ability to process complex information.
We show how LLMs can provide a novel interface between clinicians and digital technologies.
We develop a new prognostic tool using automated machine learning.
- Score: 69.02059202720073
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Digital health tools have the potential to significantly improve the delivery
of healthcare services. However, their adoption remains comparatively limited
due, in part, to challenges surrounding usability and trust. Large Language
Models (LLMs) have emerged as general-purpose models with the ability to
process complex information and produce human-quality text, presenting a wealth
of potential applications in healthcare. Directly applying LLMs in clinical
settings is not straightforward, however, with LLMs susceptible to providing
inconsistent or nonsensical answers. We demonstrate how LLM-based systems can
utilize external tools and provide a novel interface between clinicians and
digital technologies. This enhances the utility and practical impact of digital
healthcare tools and AI models while addressing current issues with using LLMs
in clinical settings such as hallucinations. We illustrate LLM-based interfaces
with the example of cardiovascular disease risk prediction. We develop a new
prognostic tool using automated machine learning and demonstrate how LLMs can
provide a unique interface to both our model and existing risk scores,
highlighting the benefit compared to traditional interfaces for digital tools.
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