AI RESEARCH
BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
arXiv CS.AI
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ArXi:2504.19467v4 Announce Type: replace-cross Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, benchmarking on large-scale real-world data such as electronic health records (EHRs) is critical, as clinical decisions are directly informed by these sources, yet current evaluations remain limited. Most existing benchmarks rely on medical exam-style questions or PubMed-derived text, failing to capture the complexity of real-world clinical data.