CancerCare Navigator: Optimizing Oncology Care with RAG.

Area of Science:

• Oncology
• Artificial Intelligence
• Medical Informatics

Background:

• Personalized medicine in oncology is advancing.
• Large Language Models (LLMs) show potential in healthcare applications.
• Evidence-based recommendations are crucial for cancer care.

Purpose of the Study:

• To introduce the CancerCare Navigator system.
• To detail its use of Retrieval-Augmented Generation (RAG) and LLMs.
• To outline research for enhancing its oncology application.

Main Methods:

• Utilizing Retrieval-Augmented Generation (RAG) for information retrieval.
• Employing Large Language Models (LLMs) for personalized recommendations.
• Focusing on breast, lung, and prostate cancer data.

Main Results:

• The CancerCare Navigator provides personalized, evidence-based recommendations.
• Current research focuses on improving retrieval precision.
• Efforts are underway to enhance contextual understanding for oncology care.

Conclusions:

• The CancerCare Navigator demonstrates a novel application of RAG and LLMs in oncology.
• Further research is essential to optimize its performance and clinical utility.
• This tool has the potential to refine oncology care delivery.