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Related Concept Videos

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Cancer therapies are various modes of treatment, such as surgery, radiation therapy, and chemotherapy that are administered to cancer patients.
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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Updated: Sep 15, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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CanerClarity App: Enhancing Cancer Data Visualization with AI-Generated Narratives.

Edgar Munoz1, Alexander D VanHelene2, Nuen Tsang Yang3

  • 1The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.

Preventive Oncology & Epidemiology
|July 17, 2025
PubMed
Summary
This summary is machine-generated.

The CancerClarity app uses Artificial Intelligence (AI) and large language models (LLMs) to transform complex cancer statistics into easy-to-understand narratives. This tool enhances communication for cancer centers, improving public health decision-making.

Keywords:
Artificial IntelligenceCancer StatisticsCancer-related CommunicationGenerative AILarge Language ModelsLow-graph Literacy

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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.