arXiv:2604. 05435v2 Announce Type: replace Abstract: Incomplete or inconsistent discharge documentation drives care fragmentation and avoidable readmissions.
The paper explores how Large Language Models can translate structured clinical data into patient-friendly narratives while maintaining clinical accuracy. Using a constrained generation framework that blends evidence conditioning with Retrieval-Augmented Generation, the authors evaluate alignment through Natural Language Inference, factuality metrics, and readability. Their results show that grounding constraints improve clinical alignment and uncertainty preservation across three LLMs, though they may increase reading difficulty and medical terminology.
arXiv:2601.03418v3 Announce Type: replace Abstract: Trustworthy clinical summarization requires every claim to be traceable to its evidence, yet existing attribution often resolves only to the senten...
arXiv:2609.13481v1 Announce Type: new Abstract: Large language models have made abstractive summarization remarkably fluent, but generated summaries can hallucinate facts, posing serious risks in bio...
EvidenceNet is a disease‑specific dataset that transforms full‑text biomedical literature into structured evidence records and graph representations, preserving study design, provenance, and quantitative support. Using an LLM‑assisted pipeline, it extracts experimentally grounded findings, normalizes entities, scores evidence quality, and links related records via typed semantic relations. The released subsets—EvidenceNet‑HCC and EvidenceNet‑CRC—contain thousands of evidence records and richly connected graphs, with high extraction and relation‑type accuracy, enabling retrieval‑augmented question answering and graph‑based tasks such as link prediction and target prioritization.
arXiv:2606. 12569v1 Announce Type: cross Abstract: We present EDEN (Emergency Department Electronic Notes), a new and unique large-scale corpus of clinical notes produced in Emergency Departments of Italian hospitals.
Although Large Language Models (LLMs) have demonstrated potential in clinical documentation, generating the rigorous, evidence-based reasoning required for unprecedented, atypical, and rare disease ca...
Scientific long-document summarization datasets commonly treat author-written abstracts as gold reference summaries, although their quality and alignment with the source article vary. At the same time, publicly available scientific summarization datasets remain limited in scale and structure for modern long-context models.
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...
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.
arXiv:2606. 26879v1 Announce Type: new Abstract: Synthetic data is increasingly used to enable the development and evaluation of AI systems in domains where access to real-world data is restricted.
arXiv:2610.08161v1 Announce Type: cross Abstract: Ambient documentation systems are rapidly gaining adoption, yet their impact on clinical note quality remains poorly characterized. We introduce MedC...