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.

翻译：人工智能（AI）与大型语言模型（LLMs）在革新医疗保健领域，特别是临床应用中，展现出巨大潜力。与此同时，能够建模和模拟复杂系统的数字孪生技术，在提升患者护理方面也日益受到关注。然而，尽管在实验性临床环境中取得了进展，AI与数字孪生在简化临床运营流程方面的潜力在很大程度上仍未得到开发。本文介绍了一种专门设计用于增强肿瘤学临床运营的新型数字孪生框架。我们提出整合多个专业化的数字孪生，例如医疗必要性孪生、护理导航孪生和临床病史孪生，以提升工作流程效率，并基于每位患者的独特数据为其提供个性化护理。此外，通过综合多种数据源并将其与美国国家综合癌症网络（NCCN）指南对齐，我们创建了一个动态的癌症护理路径——一个持续演化的知识库，使得这些数字孪生能够提供精确、量身定制的临床建议。