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.

翻译：数字健康工具有望显著改善医疗服务的交付方式。然而，由于可用性和信任度等方面的挑战，其采用率仍然相对有限。近年来，大语言模型作为通用型模型崭露头角，能够处理复杂信息并生成人类水准的文本，在医疗领域展现出丰富的应用潜力。但在临床环境中直接应用大语言模型并非易事，这类模型容易生成不一致或毫无意义的回答。我们描述了基于大语言模型的系统如何利用外部工具，在临床医生与数字技术之间提供一种全新的界面。这种方法不仅提升了数字医疗工具与人工智能模型的实用性和实际影响力，同时解决了当前大语言模型在临床应用中面临的问题，例如幻觉现象。我们通过心血管疾病和糖尿病风险预测的实例阐明了基于大语言模型的界面，突显了其相比传统数字工具界面的优势。