arXiv AI July 14, 2026 By Robert Williams

Faithful by Design: Evaluating and Improving LLM-Generated Clinical Trial Summaries for Multi-Stakeholder Audiences

arXiv:2607. 09932v1 Announce Type: cross Abstract: Large language models are increasingly used to summarize clinical trial results for healthcare providers, patients, and payers, but their tendency to hallucinate poses significant risks in this high-stakes context.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 13

A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench

arXiv:2608. 12138v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in high-income settings.

By Praveen Reddy, Charuta Mandke, Suvrankar Datta, Sarah Khan, Siddharth Reddy Anthireddy, Shitij Arora, Vishal Singh

llms rag benchmarks

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 Machine Learning

Jul 30

$\texttt{AMEND++}$: Benchmarking Eligibility Criteria Amendments in Clinical Trials

arXiv:2601. 06300v2 Announce Type: replace-cross Abstract: Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly amended component.

By Trisha Das, Mandis Beigi, Jacob Aptekar, Jimeng Sun

llms nlp benchmarks

arXiv AI

2d ago

Toward Better Assessment of LLMs' Performance in Clinical Error Detection

arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.

By Yifan Zhang, Rahmatollah Beheshti

llms nlp benchmarks safety

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

Jun 2

A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models

arXiv:2606. 00027v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed across healthcare, yet existing benchmarks fail to capture model behavior under adversarial or ethically complex conditions common in clinical practice.

By Andrei Marian Feier, Veysel Kocaman, Yigit Gul, Ahmet Korkmaz, Alexander Thomas, Aleksei Zakharov, Jay Gil, Mehmet Butgul, David Talby

llms benchmarks safety

Related stories

A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench

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

$\texttt{AMEND++}$: Benchmarking Eligibility Criteria Amendments in Clinical Trials

Toward Better Assessment of LLMs' Performance in Clinical Error Detection

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

A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models