Redefining Digital Health Interfaces with Large Language Models

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. Recently, 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, with LLMs susceptible to providing inconsistent or nonsensical answers. We describe how LLM-based systems can utilize external tools to 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 LLM in clinical settings such as hallucinations. We illustrate LLM-based interfaces with examples from cardiovascular disease and diabetes risk prediction, highlighting the benefit compared to traditional interfaces for digital tools.