标题： 医学中的整体人工智能；性能和可解释性的提升 显示英文标题

标题： Holistic Artificial Intelligence in Medicine; improved performance and explainability

摘要： 随着在医学中部署人工智能的兴趣不断增加，我们之前介绍了HAIM（整体医学人工智能），这是一个融合多模态数据以解决下游临床任务的框架。 然而，HAIM以与任务无关的方式使用数据，并缺乏可解释性。 为了解决这些限制，我们引入了xHAIM（可解释的HAIM），这是一种利用生成式人工智能的新框架，通过四个结构化步骤来增强预测和可解释性：(1) 自动识别跨模态的任务相关患者数据，(2) 生成全面的患者摘要，(3) 使用这些摘要进行改进的预测建模，以及(4) 通过将预测与患者特定的医学知识联系起来提供临床解释。 在HAIM-MIMIC-MM数据集上评估，xHAIM在胸部病理学和手术任务中的平均AUC从79.9%提高到90.3%。 重要的是，xHAIM将人工智能从一个黑箱预测器转变为可解释的决策支持系统，使临床医生能够交互式地将预测追溯到相关患者数据，弥合人工智能进步与临床实用性之间的差距。 显示英文摘要

摘要： With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.