LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making
Researchers have introduced LongMedBench, a real-world EHR-based benchmark designed to evaluate AI agents in long-horizon clinical decision-making. The study reveals that while current LLMs excel at handling explicit timestamps, they still struggle with implicit temporal reasoning.
Computer Science > Artificial Intelligence
Title:LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making
View PDF HTML (experimental)Abstract:In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making. Prior evaluations of LLM-based medical agents have largely emphasized short-context knowledge QA and tool use. However, real-world medical care is inherently longitudinal, and clinicians must aggregate evidence across repeated visits, tests, and evolving treatments. Therefore, long-horizon interaction is essential for realistic assessment. LongMedBench is constructed via a reproducible pipeline that integrates MIMIC-IV admission records and clinical notes into time-series event streams and long-context memory datasets, enabling long-horizon, multi-session interactions between agents and a clinical environment. It comprises 335 patients, with 19.72 inpatient visits per patient on average and 44.91 medical events per visit. Guided by the long-horizon decision process, we propose an evaluation taxonomy with three suites: fact-based QA, temporal reasoning, and long-horizon decision-making. This taxonomy measures how agents understand and leverage historical patient information over extended horizons. Our experiments show that while recent LLMs can make good use of explicit timestamps, they have challenges in implicit time inference; The RAG and agent memory system can improve the performance of information retrieval tasks, but the performance of decision-making tasks is highly dependent on the model's immediate context.
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Source: arXiv cs.AI Recent
