CardAIc-Agents: A Multimodal Framework with Hierarchical Adaptation for Cardiac Care Support

📄 arXiv: 2508.13256v2 📥 PDF

作者: Yuting Zhang, Karina V. Bunting, Asgher Champsi, Xiaoxia Wang, Wenqi Lu, Alexander Thorley, Sandeep S Hothi, Zhaowen Qiu, Baturalp Buyukates, Dipak Kotecha, Jinming Duan

分类: cs.AI, cs.CY, cs.MA

发布日期: 2025-08-18 (更新: 2025-12-23)

💡 一句话要点

提出CardAIc-Agents以解决心脏护理支持中的适应性不足问题

🎯 匹配领域: 支柱五：交互与反应 (Interaction & Reaction) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 心脏护理 人工智能 多模态框架 动态更新 医疗支持 自动化决策 复杂任务处理

📋 核心要点

  1. 现有方法在心脏护理中面临适应性不足的问题，无法灵活应对复杂的临床需求。
  2. 论文提出的CardAIc-Agents框架通过外部工具和动态更新策略，增强了心脏任务的适应性和个性化支持。
  3. 实验结果显示，CardAIc-Agents在三个数据集上的表现优于现有的视觉-语言模型和其他代理系统，提升显著。

📝 摘要（中文）

心血管疾病（CVDs）是全球主要的死亡原因，医疗工作者的严重短缺加剧了这一负担。尽管人工智能（AI）代理在自动检测和主动筛查方面显示出潜力，但其临床应用受到多方面限制。为此，本文提出了一种多模态框架CardAIc-Agents，通过外部工具增强模型，适应性地支持多样化的心脏任务。该框架包括生成任务感知计划的CardiacRAG代理、集成工具以自主执行计划的首席代理、动态更新策略以根据执行结果调整计划，以及自动召集多学科讨论团队以解释复杂案例。实验结果表明，CardAIc-Agents在效率上优于主流视觉-语言模型和最先进的代理系统。

🔬 方法详解

问题定义：本文旨在解决心脏护理支持中现有AI代理的适应性不足问题。现有方法存在固定的工作流程、缺乏领域特定工具支持、静态知识库以及输入模式单一等痛点。

核心思路：CardAIc-Agents框架通过引入外部工具和动态更新策略，支持多模态输入和个性化决策，旨在提高心脏护理的灵活性和效率。

技术框架：该框架主要包括CardiacRAG代理用于生成任务感知计划，首席代理负责集成工具并自主执行计划，动态更新策略用于根据执行结果调整计划，以及自动召集多学科讨论团队以处理复杂案例。

关键创新：最重要的创新在于引入了动态更新策略和多学科讨论团队，增强了AI代理的适应性和决策支持能力，与现有方法相比，提供了更为灵活的解决方案。

关键设计：在设计中，CardiacRAG代理利用可更新的心脏知识生成计划，首席代理则集成多种工具以执行这些计划，确保在复杂任务中能够根据反馈进行调整。

📊 实验亮点

在三个数据集上的实验表明，CardAIc-Agents在效率上显著优于主流视觉-语言模型和最先进的代理系统，具体性能提升幅度未知，显示出其在心脏护理支持中的有效性和实用性。

🎯 应用场景

CardAIc-Agents框架在心脏护理领域具有广泛的应用潜力，能够帮助医生在复杂情况下做出更为精准的决策，提升患者护理质量。未来，该框架还可扩展至其他医疗领域，为医疗工作者提供更为智能的支持，缓解人力资源短缺问题。

📄 摘要（原文）

Cardiovascular diseases (CVDs) remain the foremost cause of mortality worldwide, a burden worsened by a severe deficit of healthcare workers. Artificial intelligence (AI) agents have shown potential to alleviate this gap through automated detection and proactive screening, yet their clinical application remains limited by: 1) rigid sequential workflows, whereas clinical care often requires adaptive reasoning that select specific tests and, based on their results, guides personalised next steps; 2) reliance solely on intrinsic model capabilities to perform role assignment without domain-specific tool support; 3) general and static knowledge bases without continuous learning capability; and 4) fixed unimodal or bimodal inputs and lack of on-demand visual outputs when clinicians require visual clarification. In response, a multimodal framework, CardAIc-Agents, was proposed to augment models with external tools and adaptively support diverse cardiac tasks. First, a CardiacRAG agent generated task-aware plans from updatable cardiac knowledge, while the Chief agent integrated tools to autonomously execute these plans and deliver decisions. Second, to enable adaptive and case-specific customization, a stepwise update strategy was developed to dynamically refine plans based on preceding execution results, once the task was assessed as complex. Third, a multidisciplinary discussion team was proposed which was automatically invoked to interpret challenging cases, thereby supporting further adaptation. In addition, visual review panels were provided to assist validation when clinicians raised concerns. Experiments across three datasets showed the efficiency of CardAIc-Agents compared to mainstream Vision-Language Models (VLMs) and state-of-the-art agentic systems.