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Related Experiment Video

Updated: Jul 31, 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 for oncological applications.

Vera Sorin^1,2,3, Yiftach Barash^4,5,6, Eli Konen^4,6

¹Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel. verasrn@gmail.com.

Journal of Cancer Research and Clinical Oncology

|May 9, 2023

Summary

This summary is machine-generated.

Large language models show promise for supporting oncologists. Familiarity with these AI tools is crucial for maximizing benefits and understanding limitations in oncology practice.

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Area of Science:

Artificial Intelligence in Oncology
Natural Language Processing Applications

Background:

Growing attention on large language models (LLMs) like ChatGPT in scientific and public spheres.
Potential for LLMs to assist oncologists in various aspects of their clinical work.

Discussion:

Oncologists need to understand LLM capabilities and limitations.
Balancing the benefits of AI support with awareness of potential risks is essential.

Key Insights:

LLMs offer novel tools for oncological support.
Informed adoption requires knowledge of both strengths and weaknesses.

Outlook:

Future integration of AI in oncology practice.
Need for ongoing education and adaptation to emerging AI technologies.