用生成式AI提升医疗AI的预测力与可解释性
Holistic Artificial Intelligence in Medicine; improved performance and explainability
- 自动筛选多模态患者数据并生成摘要
- 预测性能AUC提升至90.3%，较原模型提高10.4个百分点
- 让医生能追溯预测依据，适合临床决策支持场景
随着人工智能在医学领域的应用日益广泛，我们此前提出了HAIM（Holistic AI in Medicine）框架，通过融合多模态数据解决下游临床任务。然而HAIM采用任务无关的数据处理方式且缺乏可解释性。为解决这一问题，本文提出xHAIM（Explainable HAIM），利用生成式AI通过四个步骤实现双重提升：(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) 基于摘要进行更优的预测建模；(4) 通过关联预测与个体化医疗知识提供临床解释。在HAIM-MIMIC-MM数据集上评估显示，xHAIM将胸腔病理与手术任务的平均AUC从79.9%提升至90.3%。重要的是，xHAIM使AI从黑箱预测工具转变为可解释的决策支持系统，支持临床医生交互式追溯预测依据，有效弥合了AI技术进步与临床实用性的鸿沟。
原文摘要 · Abstract (English)
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.
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