Back

Assessing the reliability of large language models for reviewing AI research in cardiac electrophysiology using the EHRA AI in EP checklist

21 November 2025 (08:00 - 18:00)
Organised by: Logo
Congress Presentation Part of: From bench to bedside: the potential roles of large language models in cardiovascular medicine Deep Learning Open Access ESC Digital & AI Summit 2025 European Society of Cardiology

About the speaker

Photo
Ms Amalia Sluha

Hannover (Germany)

6 More presentations in this session

Presentation thumbnail
Clinical performance and readability evaluation of large language models for patient communication in heart failure and cardiomyopathies
Doctor C. Reich (Heidelberg, DE)
Abstract Slides
Presentation thumbnail
Large language model (LLM)-based agentic artificial intelligence tool streamlines research processes in biomarker studies: a proof of concept
Ms Y. Ye (Copenhagen, DK)
Abstract Slides
Presentation thumbnail
DigiLearnHF - an LLM-enhanced digital learning program for patients with implantable defibrillators and heart failure
Ms A. Sluha (Hannover, DE)
Abstract Slides
Presentation thumbnail
CARMINA: optimizing low-parameter language models for high-quality cardiovascular research assistance
Mr J. Alvarez-Arenas (Madrid, ES)
Abstract Slides
Presentation thumbnail
Accuracy of commercial large language models in cardiovascular risk stratification: a vignette-based pilot study
Doctor J. Ferreira Santos (Setubal, PT)
Abstract Slides

Access the full session

Session thumbnail
From bench to bedside: the potential roles of large language models in cardiovascular medicine

The Event

Event poster

ESC Digital & AI Summit 2025

21 November - 22 November 2025
You may be interested in
thumbnail
Congress Session
Evolutions in arrythmia and syncope management
thumbnail
Congress Session
Artificial intelligence in heart failure research and clinical practice
thumbnail
Congress Session
How to implement a successful telemedicine program in heart failure

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources