Future Querying: Can LLMs Serve as Implicit Medical World Models?

• URL: http://arxiv.org/abs/2608.23248v1
• Date: Mon, 24 Aug 2026 13:41:12 GMT
• Title: Future Querying: Can LLMs Serve as Implicit Medical World Models?
• Abstract summary: We introduce future querying, a paradigm that probes whether large language models (LLMs) can function as implicit medical world models.<n>Our framework operates on unstructured clinical documentation using endpoint-agnostic training.<n>We show that small, locally fine-tuned open-weight models can match or approach larger proprietary systems.
• Score: 3.3341114691426257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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