发表机构
imec, AI-labs（imec人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出未来查询范式，探究LLMs能否作为隐式医学世界模型，其框架可基于非结构化临床文档运行，经微调的小型开放权重模型性能接近专有系统，在相关数据集上验证了LLMs可捕捉临床动态。
AI 中文摘要
传统临床预测模型依赖于特定任务的流程和经过整理的结构化数据，其扩展性差且未能充分利用非结构化文本。为解决这一问题，我们提出未来查询这一范式，通过评估大语言模型（LLMs）回答关于患者未来的时间索引临床查询的能力，探究其能否作为隐式医学世界模型发挥作用。我们的框架基于非结构化临床文档运行，采用与端点无关的训练方式，使单个模型无需手动特征工程或针对特定任务的重新训练，即可回答关于患者轨迹的各类临床查询。我们证明，经本地微调的小型开放权重模型可达到或接近更大的专有系统的性能，这使得该框架适用于隐私保护的本地部署。在新的合成医学报告数据集和来自MIMIC-IV数据集的真实ICU记录上进行评估后，我们的结果提供了令人鼓舞的证据，表明LLMs能够捕捉临床动态的相关方面。
英文摘要
Traditional clinical prediction models rely on task-specific pipelines and curated, structured data, which scale poorly and underutilize unstructured text. To address this, we introduce future querying, a paradigm that probes whether large language models (LLMs) can function as implicit medical world models by evaluating their ability to answer time-indexed clinical queries about a patient's future. Our framework operates on unstructured clinical documentation using endpoint-agnostic training, enabling a single model to answer diverse clinical queries over patient trajectories without manual feature engineering or task-specific retraining. We show that small, locally fine-tuned open-weight models can match or approach larger proprietary systems, making the framework suitable for privacy-preserving, on-premise deployment. Evaluated on a new synthetic medical reports dataset and real ICU notes from the MIMIC-IV dataset, our results provide encouraging evidence that LLMs can capture aspects of clinical dynamics.
CommentsThis paper is accepted at The 1st MICCAI Workshop on Medical World Models (MICCAI-2026)
