用检索增强生成框架，整合多源临床数据预测骨髓炎预后。
RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis
- 构建基于领域检索库的RAG框架，融合影像、手术记录与随访文本。
- 在真实病例上展现良好预测效果，结果与临床实践高度一致。
- 提升模型可解释性，适合临床决策支持场景使用。
慢性骨髓炎因高复发率和复杂的术后恢复轨迹，带来重大预后挑战。传统评估依赖人工评分系统，限制了临床实践中的可扩展性、效率与一致性。此外，临床数据异质性强，现有多模态学习方法需对齐输入且依赖大量标注数据，难以应对。本文提出RAG4Outcome，一种用于慢性骨髓炎预后预测的检索增强生成（RAG）框架。该方法整合PET-CT影像报告、结构化手术与诊断记录、以及非结构化随访笔记，构建统一预测流程。通过结合领域特定检索语料库与专家引导提示，实现更具可解释性、证据支撑和临床可信度的预后判断。在真实病例上的初步结果表明，该框架具有显著有效性与临床契合度，展现出辅助感染管理与术后决策支持的巨大潜力。
原文摘要 · Abstract (English)
Chronic osteomyelitis presents substantial prognostic challenges due to its high recurrence risk and complex postoperative recovery trajectories. Traditional assessment often relies on manual scoring systems, which limit scalability, efficiency, and consistency in clinical practice. Furthermore, the heterogeneous nature of clinical data poses challenges for current multimodal learning approaches that require aligned inputs and large annotated datasets. In this work, we propose RAG4Outcome, a retrieval-augmented generation (RAG) framework for prognostic prediction in chronic osteomyelitis. Our method integrates multimodal clinical data, including PET-CT imaging reports, structured surgical and diagnostic records, and unstructured follow-up notes, into a unified prediction pipeline. By combining a domain-specific retrieval corpus with expert-guided prompting, the framework enables more interpretable, evidence-grounded, and clinically reliable prognosis. Preliminary results on real-world cases demonstrate promising effectiveness and clinical alignment, highlighting the potential of RAG4Outcome for AI-assisted infection management and postoperative decision support.
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