output
20212026
most citedAdapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization

755 citations

7 papers

cs.CR2026

Blockchain-Enabled Artificial Intelligence and AI Agents for Secure Data Sharing and Cybersecurity Applications

Harsh Verma

Blockchain and artificial intelligence (AI) are converging into a single infrastructural layer for securing data sharing, model integrity, and autonomous decision-making across dis…

cs.DL2026

The em-dash em-beds in Congress: A population-level rise in em-dash frequency in U.S. congressional press releases at the dawn of the large-language-model era, 2021-2025

Przemysław Czuma

Large language models (LLMs) can leave small stylistic traces in text written with their help. The most discussed is the em-dash (U+2014), especially the unspaced form word---word,…

q-bio.MN2025★ 1 cited

Enumeration of Autocatalytic Subsystems in Large Chemical Reaction Networks

Richard Golnik, Thomas Gatter, Peter F. Stadler +1

Autocatalysis is an important feature of metabolic networks, contributing crucially to the self-maintenance of organisms. Autocatalytic subsystems of chemical reaction networks (CR…

cs.CL2024★ 31 cited

A dataset and benchmark for hospital course summarization with adapted large language models

Asad Aali, Dave Van Veen, Yamin Ishraq Arefeen +9

Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automati…

cs.CL2023★ 755 cited

Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization

Dave Van Veen, Cara Van Uden, Louis Blankemeier +16

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large langua…

cs.LO2023

Multimodality in the Hypergraph Lambek Calculus

Tikhon Pshenitsyn

The multimodal Lambek calculus is an extension of the Lambek calculus that includes several product operations (some of them being commutative or/and associative), unary modalities…

eess.IV2021★ 72 cited

Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation

Rikiya Yamashita, Jin Long, Snikitha Banda +2

Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various…