ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1623339
This article is part of the Research TopicGenAI in Healthcare: Technologies, Applications and EvaluationView all 8 articles
Redefining Digital Health Interfaces with Large Language Models
Provisionally accepted
- 1University of Oxford, Oxford, United Kingdom
- 2University of Cambridge, Cambridge, United Kingdom
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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 appli-cations in healthcare. Directly applying LLMs in clinical settings is not straightforward, however, as LLMs are susceptible to providing inconsistent or nonsensical answers. We demonstrate how LLM-based systems, with LLMs acting as agents, 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 examples of cardiovascular disease and stroke risk prediction, quantitatively assessing their performance and highlighting the benefit compared to traditional interfaces for digital tools.
Keywords: LLM, Large Language Model, risk score, cardiovascular disease, LLM agents
Received: 05 May 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Imrie, Rauba and Van Der Schaar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Fergus Imrie, University of Oxford, Oxford, United Kingdom
Mihaela Van Der Schaar, University of Cambridge, Cambridge, United Kingdom
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