Are Large Language Models the Best Natural Language Processing Solution for Clinical Applications?

2025 Informatics Summit On Demand

Presentation Time: 03:30 PM - 05:00 PM

Abstract Keywords: Natural Language Processing, Machine Learning, Generative AI, and Predictive Modeling, Knowledge Representation, Management, or Engineering
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Harnessing the Power of Large Language Models in Health Data Science

The purpose of the panel is to discuss and inform the researchers and practitioners about the differences between task-specific models versus general-purpose models, namely, between Named Entity Recognition (NER) models and Large Language Models (LLM). NER is an important task in natural language processing. Due to the complexity and ever-changing nature of language, recognizing a named entity requires a significant effort in model training in addition to the pre-trained models. LLMs on the other hand, do not require such effort. The panel will elucidate the differences between task-specific NER models and LLM on entity recognition tasks and showcase the comparisons between the two models in various use cases. In addition, the panel will offer tips and lessons learned from working with both task-specific NER and LLM in a large corpus of texts.
Moderator:
Elise Berliner, PhD
Oracle Life Sciences

Speaker(s):
Ali Soroush, MD, MS
Icahn School of Medicine at Mount Sinai

David Talby, Dr.


Hongfang Liu, PhD
University of Texas Health Science Center at Houston

Author(s):