用数字孪生技术优化癌症临床流程，实现个性化精准医疗。
Digital Twin Ecosystem for Oncology Clinical Operations
- 构建医疗必要性、护理导航、病史等多类数字孪生模型协同工作。
- 整合多源数据并对接NCCN指南，生成动态癌症诊疗路径。
- 适合肿瘤临床系统设计者与智能医疗研究者参考。
人工智能（AI）与大语言模型（LLMs）在医疗领域具有巨大潜力，尤其在临床应用中。与此同时，数字孪生技术通过建模与仿真复杂系统，在提升患者照护方面日益受到关注。然而，尽管在实验性临床环境中取得进展，AI与数字孪生在优化临床运营方面的潜力仍远未被充分挖掘。本文提出一种专为增强肿瘤临床运营而设计的新数字孪生框架。我们引入多种专用数字孪生模型，如医疗必要性孪生、护理导航孪生和临床病史孪生，以基于患者独特数据提升工作流程效率并实现个性化照护。此外，通过融合多源数据并与其对齐国家综合癌症网络（NCCN）指南，我们构建了一个动态癌症诊疗路径，一个持续演化的知识库，使这些数字孪生能够提供精确、定制化的临床建议。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) and Large Language Models (LLMs) hold significant promise in revolutionizing healthcare, especially in clinical applications. Simultaneously, Digital Twin technology, which models and simulates complex systems, has gained traction in enhancing patient care. However, despite the advances in experimental clinical settings, the potential of AI and digital twins to streamline clinical operations remains largely untapped. This paper introduces a novel digital twin framework specifically designed to enhance oncology clinical operations. We propose the integration of multiple specialized digital twins, such as the Medical Necessity Twin, Care Navigator Twin, and Clinical History Twin, to enhance workflow efficiency and personalize care for each patient based on their unique data. Furthermore, by synthesizing multiple data sources and aligning them with the National Comprehensive Cancer Network (NCCN) guidelines, we create a dynamic Cancer Care Path, a continuously evolving knowledge base that enables these digital twins to provide precise, tailored clinical recommendations.
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