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Introduction

Date: Tuesday, May 14, 2024
Time: 15:00 - 16:00 CEST
Duration: 1 hour

Clinical Large Language Models (CLLMs) are now a tangible reality in healthcare. Beyond their application in medical tasks, like drafting discharge summaries, they offer significant potential for workflow optimization and business intelligence. At Essen University Hospital, these technologies are being developed and integrated directly into patient care.

In this webinar you will learn about recent advancements in the application of CLLMs:

  • CLUE: A new benchmark to assess the clinical language understanding of LLMs
  • RAW: Retrieval augmented writing of clinical documents
  • An advanced medical vision language model that excels in visual downstream tasks
  • FLAME (Fast, Language-Agnostic Metrics Engine) to enable quick and efficient medical data querying for informed decision-making


Learn more about NVIDIA LLM and Generative AI Solutions or visit the Clinic’s page for more details.

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Transforming Medical Workflows with AI: A Deep Dive into CLLMs

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DGX Station Datasheet

Get a quick low-down and technical specs for the DGX Station.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.

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Speakers

Kamyar Arzideh
Data Scientist and Researcher, Essen University Hospital
Kamyar completed his master's degree in Medical Informatics at the University of Applied Sciences in Dortmund. The topic of his master's thesis was the development and evaluation of a Natural Language Processing (NLP) pipeline to extract FHIR resources from unstructured medical data. He works at the Institute for Artificial Intelligence in Medicine in Essen as a Data Scientist and Researcher. The main focus of his research is the domain adaptation and fine-tuning of Large Language Models in the clinical context.
Amin Dada
NLP Team Lead, Essen University Hospital
After graduating with a master’s in computer science, Amin joined the Institute for Artificial Intelligence in Medicine as a Ph.D. candidate. He is currently leading the NLP team of the Medical Machine Learning group. His projects focus on adapting and evaluating Large Language Models for clinical use and extending their capabilities to multimodal settings.
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Date & Time: Wednesday, April 22, 2018