Redefining Digital Health Interfaces with Large Language Models

TL;DR

This paper explores how large language models can improve digital health interfaces by integrating external tools and providing clinicians with more effective, trustworthy, and user-friendly access to healthcare AI models, exemplified through cardiovascular risk prediction.

Contribution

It introduces a novel LLM-based interface that enhances digital health tools by addressing trust and usability issues and demonstrates its application in cardiovascular risk assessment.

Findings

LLMs can effectively interface with external healthcare tools.

The proposed system improves clinician interaction with AI models.

Enhanced trust and usability in digital health tools achieved.

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.