CanerClarity App: Enhancing Cancer Data Visualization with AI-Generated Narratives

Area of Science:

• Public Health Informatics
• Health Communication
• Data Science

Background:

• Community cancer centers struggle with accessing and communicating vital cancer data due to limited resources.
• Effective communication of health information is crucial for patients and community members.

Purpose of the Study:

• To introduce the CancerClarity app, an innovative tool designed to bridge the gap in cancer data accessibility and communication.
• To leverage Artificial Intelligence (AI) and large language models (LLMs) for generating accessible cancer data narratives.

Main Methods:

• The CancerClarity app utilizes LLM prompting within the R Shiny framework, integrating data from Cancer InFocus.
• It provides interactive data visualization of cancer incidence, mortality, and health determinants across U.S. counties.

Main Results:

• The app integrates LLMs via API for real-time, tailored narratives, making complex cancer data accessible.
• It offers a cost-effective solution for cancer centers to identify catchment areas and assess population cancer burden.

Conclusions:

• AI-driven narratives enhance public health decision-making by improving the communication of cancer data.
• Future development includes Retrieval Augmented Generation (RAG) for enhanced AI responses and evidence-based guidance.