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A Living Benchmark for Information Retrieval from Electronic Health Records

Paper recorded by Signals 4 on 2026-09-24 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-24 on arXiv · recorded by Signals 4 on 2026-09-25

Category: cs.AI · 人工智能 · first seen 2026-09-25

Abstract

Large language model (LLM)-based clinical assistants are increasingly being integrated into electronic health record (EHR) systems, transforming how clinicians retrieve and synthesize information from patient records. Their safety and utility depend on rigorous evaluation, yet existing benchmarks are manually curated, costly to update, and rapidly become obsolete with evolving technological advanc

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#13 most recent of 400 cs.AI papers we have recorded · ↑ newer: Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV S · ↓ older: ExplorationBench: Measuring AI Systems' Exploration in Verifiable Alie
Cite this page: A Living Benchmark for Information Retrieval from Electronic Health Records: the #13 most recent of 400 cs.AI papers we have recorded (as of 2026-09-24). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/a-living-benchmark-for-information-retrieval-from-electronic-health-records.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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