用专用大模型提升结核病患者沟通效率，助力治疗完成。
Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication
- 将定制大模型嵌入数字依从性系统，实现智能对话支持。
- 在人机协同框架下，增强患者参与度与治疗依从性。
- 适合资源有限地区医疗团队，提升远程照护能力。
结核病（TB）是全球传染病致死的首要原因，主要集中在低收入和中等收入国家。这些地区医疗资源匮乏、医患比例失衡，制约了对患者的持续支持、沟通及治疗完成率。为弥合这一差距，本文提出将专用大语言模型集成至高效的数字依从性技术中，以增强治疗支持者与患者之间的互动沟通。该人工智能方法在“人在回路”框架下运行，旨在提升患者参与度，改善结核病治疗结局。
原文摘要 · Abstract (English)
Tuberculosis (TB) is the leading cause of death from an infectious disease globally, with the highest burden in low- and middle-income countries. In these regions, limited healthcare access and high patient-to-provider ratios impede effective patient support, communication, and treatment completion. To bridge this gap, we propose integrating a specialized Large Language Model into an efficacious digital adherence technology to augment interactive communication with treatment supporters. This AI-powered approach, operating within a human-in-the-loop framework, aims to enhance patient engagement and improve TB treatment outcomes.
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