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.

翻译：数字健康工具有望显著改善医疗服务的提供方式。然而，其应用仍然相对有限，部分原因在于可用性和信任方面的挑战。大型语言模型（LLMs）作为通用模型出现，能够处理复杂信息并生成人类水平的文本，在医疗领域展现出丰富的潜在应用。然而，在临床环境中直接应用LLMs并非易事，因为LLMs容易提供不一致或毫无意义的回答。我们展示了基于LLM的系统如何利用外部工具，并为临床医生与数字技术之间提供一种新颖的界面。这增强了数字医疗工具和人工智能模型的实用性与实际影响，同时解决了当前在临床环境中使用LLM时存在的幻觉等问题。我们以心血管疾病风险预测为例，说明了基于LLM的界面。我们利用自动化机器学习开发了一种新的预后工具，并展示了LLM如何为我们的模型及现有风险评分提供独特界面，突显了其相比传统数字工具界面的优势。