The paper examines how Retrieval-Augmented Generation (RAG) and Named Entity Recognition (NER) affect the quality of lay summaries of radiology reports. Using a framework that extracts clinically relevant findings via NER and grounds them with RAG, the authors evaluate few‑shot and fine‑tuned versions of Qwen and BioBART. Results show that NER consistently improves readability and overall quality, RAG alone offers no benefit and can introduce hallucinations, and the best performance comes from fine‑tuned BioBART with NER.
By Egecan \c{C}elik Evgin, \.Ilknur Karadeniz, Olcay Taner Y{\i}ld{\i}z
arXiv:2609.14819v1 Announce Type: cross
Abstract: Large language models (LLMs) have a growing range of applications in medicine, and their evaluation is critical for ensuring they provide benefit and...
By Suzannah E McKinney, Phuc Vu, Samuel A Justice, Christopher Humphries, Alyssa Pradhan, Timothy J Keyes, Bernardo C Bizzo, Keith J Dreyer, Sarah F Mercaldo, James M Hillis
arXiv:2608.28624v1 Announce Type: cross
Abstract: Accurate interpretation of single-visit and longitudinal clinical assessments for Parkinson's disease is time-consuming and often depends on speciali...
By Sana Alamgeera, Denise Goberta, Muhammad Irshad, Anne H. H. Ngu
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
arXiv:2608.30022v1 Announce Type: new
Abstract: Introduction: NICE guidelines provide evidence-based recommendations for clinical care but remain largely in unstructured natural language. Existing ap...
By Ashvin Gupta, Denys Prociuk, Alessandra Russo, Brendan C. Delaney
The study investigates whether using only abstracts of biomedical articles can produce lay summaries that match the quality of those derived from full texts, and whether few‑shot learning can replace fine‑tuning large language models. Experiments on two public datasets with two recent models show that abstract‑based summarization outperforms full‑text approaches in readability, factual consistency, semantic similarity, and runtime efficiency. Abstract fine‑tuning is 4.70 times faster and abstract zero‑ and few‑shot prompting is 5.31 times faster than full‑text methods, indicating that abstract‑based prompting reduces computational cost while preserving summary quality.
By Egecan \c{C}elik Evgin, \.{I}lknur Karadeniz, Olcay Taner Y{\i}ld{\i}z
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.
By Robert Williams
arXiv:2606. 19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs.
By Soheyl Bateni, Maryam Abdolali
The paper introduces BASSE, a multilingual meta‑evaluation dataset containing 2,040 human‑rated abstractive summaries produced manually or by five LLMs with four prompts. Annotators scored each summary on coherence, consistency, fluency, relevance, and 5W1H using a 5‑point Likert scale. Benchmarking shows proprietary LLM‑judge models best align with human judgments, followed by criteria‑specific automatic metrics, while open‑source judge LLMs perform poorly.
By Jeremy Barnes, Naiara Perez, Alba Bonet-Jover, Bego\~na Altuna
MTDiag is a newly released multi-turn diagnostic dialogue dataset designed to evaluate large language models (LLMs) in clinically meaningful ways. It is built from DDXPlus, MIMIC-IV, and AJCR case reports, covering both common emergency department presentations and rare conditions, and normalizes cases into a canonical schema using UMLS concept identifiers and ICD-10 codes. The dataset includes a UserLM‑8B utterance‑generation pipeline and physician‑validated natural‑language utterances, and introduces clinical knowledge‑grounded metrics that go beyond simple diagnostic accuracy for multi‑turn differential diagnosis tasks.
By Pia Chouayfati, Alexander M. Fichtl, Miriam Ansch\"utz, George Doumat, Georg Groh
arXiv:2608. 04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult.
By Karen Lee, Dhanashree Balaram, Seojun Shon, Umair Rasheed
arXiv:2606. 08969v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medically important information and introduce unsupported claims.
By Suhana Bedi, Bridget Lin, Anson Y. Zhou, Chloe O. Stanwyck, Jenelle A. Jindal, Sanmi Koyejo, David Stutz, Nigam H. Shah