arXiv AI

Examine Clinicians' Modification of Hedging Language in Ambient AI Documentation: A Comparative Study of AI Drafts and Final Notes

arXiv:2606. 00018v1 Announce Type: cross Abstract: Ambient AI documentation systems generate clinical note drafts that clinicians frequently revise before signing off into electronic health records, yet how these edits alter hedging language remains unclear.

arXiv AI

Jun 2

Understanding Stigmatizing Language in Clinical Documentation: A Paired Comparison of Ambient AI Drafts and Clinician Finalized Notes

arXiv:2606. 00019v1 Announce Type: cross Abstract: Ambient artificial intelligence (AI) documentation tools are increasingly deployed to reduce clinician documentation burden, but their implications for biased language in clinical notes remain unclear.

By Yiliang Zhou, Yawen Guo, Sairam Sutari, Jasmine Dhillon, Alexandra L. Beck, Emilie Chow, Steven Tam, Danielle Perret, Deepti Pandita, Gelareh Sadigh, Archana J. McEligot, Kai Zheng

nlp

arXiv AI

Jul 31

The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness

arXiv:2503. 10647v2 Announce Type: replace-cross Abstract: This study evaluated the diagnostic reliability of two Large Language Models (LLMs), Google Gemini 2.

By Krishna Subedi

llms rag robotics

arXiv AI

Jul 21

Real-World Evaluation of an AI Agent Drafting Translational Impact Summaries

arXiv:2607. 16989v1 Announce Type: cross Abstract: Introduction.

By Mohammad Arvan, Amber E. Osterholt, Bailee Rue, Yuvaneswaren Ramakrishnan Sureshbabu, Krishna Riteshkumar Patel, Rebecca T. Feinstein, Bethany C. Bray, Niranjan S. Karnik

agents

arXiv AI

Aug 6

Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications

arXiv:2509. 08604v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to enhance domain-specific accuracy and safety.

By Anran Li, Lingfei Qian, Mengmeng Du, Yu Yin, Yan Hu, Zihao Sun, Yihang Fu, Hyunjae Kim, Erica Stutz, Xuguang Ai, Qianqian Xie, Rui Zhu, Jimin Huang, Yifan Yang, Siru Liu, Yih-Chung Tham, Lucila Ohno-Machado, Hyunghoon Cho, Zhiyong Lu, Hua Xu, Qingyu Chen

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jun 5

Measuring the sensitivity of LLM-based structured extraction to prompt, model, and schema choices in clinical discharge summaries

arXiv:2606. 05970v1 Announce Type: cross Abstract: Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream configuration choices is less understood than their accuracy on fixed benchmarks.

By Martin Murin

llms benchmarks

arXiv AI

Aug 12

Most biomedical publications show signs of LLM-assisted writing

arXiv:2608. 10715v1 Announce Type: cross Abstract: Over the past several years, LLM-powered chatbots and agents have become widely used as a tool for academic writing.

By Lena Holzwarth, Rita Gonz\'alez-M\'arquez, Dmitry Kobak

llms agents

arXiv AI

Jun 30

Em-ergence of the em-dash: a population-level rise in em-dash frequency in medRxiv preprints at the dawn of the large-language-model era

arXiv:2606. 29540v1 Announce Type: cross Abstract: Large language models (LLMs) can leave subtle stylistic traces in assisted text; one of the most cited is the em-dash (Unicode U+2014).

By Przemys{\l}aw Czuma

llms

arXiv Machine Learning

Jun 5

Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection

arXiv:2606. 06481v1 Announce Type: cross Abstract: As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but instead result from progressive human-AI co-editing.

By Sondos Mahmoud Bsharat, Jiacheng Liu, Xiaohan Zhao, Tianjun Yao, Xinyi Shang, Yi Tang, Jiacheng Cui, Ahmed Elhagry, Salwa K. Al Khatib, Hao Li, Salman Khan, Zhiqiang Shen

benchmarks

arXiv AI

Jul 13

Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs

arXiv:2508. 14817v2 Announce Type: replace-cross Abstract: Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).

By Skatje Myers, Dmitriy Dligach, Timothy A. Miller, Samantha Barr, James Landefeld, Yanjun Gao, Matthew Churpek, Anoop Mayampurath, Majid Afshar

llms rag

arXiv AI

Jun 6

Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison

arXiv:2606. 05436v1 Announce Type: new Abstract: Summarizing the latest medical literature to guide clinical decision-making is essential for evidence-based medicine and high-quality patient care.

By Alejandro Lozano, Keiko Ihara, Ping-Hao Yang, Carrie E. Robertson, Jennifer Stern, Allan Purdy, Hsiangkuo Yuan, Pengfei Zhang, Yulia Orlova, Olga Fermo, Jennifer Hranilovich, Fred Cohen, Todd J. Schwedt, Jenelle A. Jindal, Serena Yeung-Levy, Chia-Chun Chiang

llms rag agents nlp benchmarks

arXiv AI

23h ago

Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review

arXiv:2507. 06185v2 Announce Type: replace-cross Abstract: In July 2025, 18 academic manuscripts on arXiv contained hidden instructions that manipulated AI-assisted peer review (indirect prompt injection).

By Zhicheng Lin

llms nlp robotics

arXiv AI

Jun 9

From `May' to `Is': Certainty Distortion in Language Model Rewriting

arXiv:2606. 07951v1 Announce Type: cross Abstract: Humans increasingly turn to Language Models (LMs) in ways that shape beliefs and drive decisions, including discussing, rewriting, and summarizing information from scientific articles, news, and medical reports.

By Catarina G Belem, Shang Wu, Hongyu Yao, Mark Steyvers, Sameer Singh, Padhraic Smyth

llms benchmarks safety

Related stories