arXiv AI By Daniel Varab, Victor Petr\'en Bach Hansen, Asbj{\o}rn W. Helge, Kevin Pelgrims, Mathias Baltzersen, Adrian Young-San Roessler, Vanessa Klungtvedt, Maximilian Brand, Lasse Krogsb{\o}ll, Henrik Cullen, Lars Maal{\o}e

Symphony for Text Generation: Benchmarking Clinical Note Generation

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arXiv Computation and Language
Sep 22

Knowledge Graph-Augmented Ambient AI for Clinical Note Generation

arXiv:2609.22239v1 Announce Type: new Abstract: Ambient AI is increasingly adopted in healthcare to automatically generate clinical notes from patient-clinician conversations, with the potential to s...

By Jakir Hossain, Yi-Fei Zhao, Hongjian Wang, Minmei Shih, Katie Leigh Mullen, Ahmad P. Tafti, Leming Zhou, Manoj Purohit, William Hogan, Jay Zeng, Elizabeth Skidmore, Yanshan Wang
arXiv AI
Jun 16

MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs

arXiv:2508. 01401v2 Announce Type: replace-cross Abstract: Physicians spend significant time documenting clinical encounters, a burden that contributes to professional burnout.

By Ahmad Rezaie Mianroodi, Amirali Rezaie, Niko Grisel Todorov, Nadine A. Friedrich, Maria P Mogollon, Alexander Hernandez-Tirado, Guillermo Lopez Garcia, Cyril Rakovski, Frank Rudzicz
Hugging Face Trending Papers
Sep 24

A Living Benchmark for Information Retrieval from Electronic Health Records

The paper introduces BRIE, a scalable framework that automatically creates question–answer pairs from longitudinal electronic health record notes, validated by nineteen clinicians. It offers a continuously maintainable benchmark for evaluating large language models in clinical settings, addressing limitations of manual, costly, and quickly outdated existing benchmarks. Experiments across nine LLMs and five inference strategies reveal that even state‑of‑the‑art systems often miss clinically important information, especially for synthesis‑heavy queries.