- 目标: 肿瘤学中的多模态深度学习模型目前受到整体设计的限制，这些设计严格耦合数据摄取, 临床路由, 和人工智能(AI) 推理。为了解决这种不灵活性,，我们提出了大型癌症助手(LCA),，这是一个与模型无关的,事后编排框架，旨在用于可扩展的临床决策支持。 - 方法: LCA 在数学上形式化为基于算法不可渗透性, 原则的 7 元组架构，确保编排逻辑严格独立于底层黑盒 AI 模型。我们引入了入门理论,，利用几何深度学习(GDL) 沿着不同的结构和医疗轴标准化多模式患者数据。该系统通过癌症切换模块动态编排数据，并通过输出标准化中间有效负载 (SIP) 有意将核心 AI 执行与不稳定的医院 IT 基础设施隔离。 - 结果: 概念验证(PoC) 跨四个技术场景验证了编排逻辑。该框架执行名义流程，编排开销可以忽略不计。它通过在 AI 模型交换, 期间保持不变的路由投影来实证证明算法的不可渗透性，并通过在注入数据异常情况下生成目标补充数据请求 (SDR) 时实现 100\% 召回率来验证严格的故障安全性。多协议执行能力也得到成功验证。 - 结论: 通过在结构上将多模式摄取与特征推断, 解耦，LCA 提供了高度适应性和模块化的编排基础。 SIP 建立了清晰的架构边界,，为下游电子病历 (EMR) 互操作性作为独立的未来范例奠定了基础。
- Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routing, and artificial intelligence (AI) inference. To address this inflexibility, we propose the Large Cancer Assistant (LCA), a model-agnostic, post-hoc orchestration framework designed for scalable clinical decision support. - Methods: The LCA is mathematically formalized as a 7-tuple architecture grounded in the principle of Algorithmic Impermeability, ensuring the orchestration logic remains strictly independent of underlying black-box AI models. We introduce the Entry Theory, leveraging Geometric Deep Learning (GDL) to standardize multimodal patient data along distinct structural and medical axes. The system dynamically orchestrates data via a Cancer Switching Module and intentionally isolates the core AI execution from volatile hospital IT infrastructures by outputting a Standardized Intermediate Payload (SIP). - Results: A Proof of Concept (PoC) validated the orchestration logic across four technical scenarios. The framework executed a nominal flow with negligible orchestration overhead. It empirically demonstrated algorithmic impermeability by maintaining an invariant routing projection during AI model swaps, and it validated strict failure-safety by achieving a 100\% recall rate in generating targeted Supplementary Data Requests (SDR) under injected data anomalies. Multi-protocol execution capability was also successfully verified. - Conclusion: By structurally decoupling multimodal ingestion from feature inference, the LCA provides a highly adaptable and modular orchestration foundation. The SIP establishes a clear architectural boundary, natively setting the stage for downstream Electronic Medical Record (EMR) interoperability as an independent future paradigm.
科目: 人工智能 (cs.AI); 机器学习 (cs.LG)
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)