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

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Large language models in cancer: potentials, risks, and safeguards.

Md Muntasir Zitu^1,2, Tuan Dung Le², Thanh Duong²

¹Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, United States.

BJR Artificial Intelligence

|January 8, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) show promise in cancer research for data analysis and patient interaction. However, careful consideration of risks like data bias and ethical issues is crucial for responsible AI integration in oncology.

Keywords:

ChatGPT artificial intelligence cancer chatbots large language models natural language processing potentials risks safeguards

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Area of Science:

Oncology
Artificial Intelligence
Natural Language Processing

Background:

Large language models (LLMs) are increasingly being explored for applications in healthcare.
The integration of AI in cancer research presents both opportunities and challenges.

Purpose of the Study:

To review the current literature on the use of LLMs in cancer research.
To analyze the capabilities, risks, and ethical considerations associated with LLMs in oncology.

Main Methods:

Systematic review of articles from PubMed, Embase, and Ovid Medline (2017-2024).
Search terms included LLMs, cancer research, risks, safeguards, and ethical issues.
Categorization of 59 included articles into quantitative, chatbot-focused, and qualitative studies.

Main Results:

LLMs demonstrate advanced natural language processing (NLP) capabilities.
Chatbot applications show potential in clinical support and cancer data management.
Qualitative research highlights risks, including data biases, and ethical concerns.

Conclusions:

LLMs, such as ChatGPT, offer potential benefits in cancer data analysis, patient interaction, and personalized treatment.
A balanced approach focusing on accuracy, ethical integrity, and data privacy is essential.
Further research and regulatory oversight are needed for the responsible application of AI in oncology.