这篇论文讲了一个叫LCA的框架，专门为肿瘤学临床决策支持设计，能灵活组合不同AI模型。测试下来零开销、100%召回率，挺实用的。
论文提出Large Cancer Assistant（LCA），一个7元组架构的编排框架，基于Algorithmic Impermeability原则，使编排逻辑与底层AI模型解耦。LCA引入Entry Theory，利用Geometric Deep Learning将多模态患者数据标准化为6个轴。系统通过Cancer Switching Module动态路由数据，输出Standardized Intermediate Payload（SIP）隔离医院IT变更。PoC在4个场景验证：名义流程编排开销接近零、AI模型替换时路由投影不变、注入数据异常下Supplementary Data Requests召回率100%、多协议执行成功。
The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology
- 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.