Digital health tools have the potential to significantly improve the delivery of healthcare services. However, their use 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 demonstrate how LLMs 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 our approach with examples from cardiovascular disease and diabetes risk prediction, highlighting the benefit compared to traditional interfaces for digital tools.

翻译：数字健康工具有望显著改善医疗服务的交付效率。然而，受限于可用性及信任度等挑战，其实际应用仍相对有限。近年来，大语言模型（LLMs）作为通用型模型崭露头角，能够处理复杂信息并生成类人质量文本，在医疗领域展现出广泛应用潜力。但将LLMs直接应用于临床环境并非易事，因其容易产生不一致或荒谬的回答。我们展示了如何通过利用外部工具，使LLMs成为临床医生与数字技术之间的新型交互界面。该方法不仅增强了数字健康工具及AI模型的实用性与实际影响力，还解决了当前LLMs在临床应用中面临的幻觉等问题。我们以心血管疾病和糖尿病风险预测为例阐释这一方法，并凸显其相较于传统数字工具界面的优势。