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Related Experiment Video

Updated: Jul 26, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Large language models and the emergence phenomena.

Vera Sorin^1,2, Eyal Klang^1,2,3

¹Department of Diagnostic Imaging, Chaim Sheba Medical Center, affiliated to the Sackler School of Medicine, Tel-Aviv University, Israel.

European Journal of Radiology Open

|June 16, 2023

Summary

This summary is machine-generated.

Emergence phenomena in large language models (LLMs) offer transformative potential for radiology data management and analysis. Radiologists should prepare for these advancements impacting medical imaging and practice.

Keywords:

ChatGPT Emergence Generative models LLMs

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Area of Science:

Artificial Intelligence in Radiology
Machine Learning in Medical Imaging

Background:

Large language models (LLMs) are advancing rapidly.
Emergence phenomena in machine learning are not yet fully understood.
The integration of AI in radiology is a growing trend.

Purpose of the Study:

To explore the potential of emergence phenomena in LLMs for radiology.
To explain LLMs and emergence in machine learning.
To discuss applications, risks, and limitations of LLMs in radiology.

Main Methods:

Perspective piece synthesizing current knowledge.
Explanation of LLMs and emergence concepts.
Identification of potential applications in radiology data management and analysis.

Main Results:

LLMs exhibit emergent capabilities with potential to revolutionize radiology.
Potential applications include enhanced data management, image analysis, and clinical decision support.
Identified risks include data privacy, algorithmic bias, and the need for robust validation.

Conclusions:

Emergence in LLMs presents significant opportunities for radiology.
Radiologists must understand and prepare for the impact of these technologies.
Proactive engagement is crucial for harnessing LLM potential responsibly in medical imaging.