# The role of artificial intelligence in advancing population-based cancer registration

## TL;DR

This paper explores how artificial intelligence can improve cancer registration by reducing workload and enhancing data analysis for better cancer control.

## Contribution

The paper introduces a forward-looking AI-enhanced framework for cancer registration and discusses challenges in integrating AI into existing systems.

## Key findings

- AI can reduce labor-intensive tasks in cancer registries by processing large datasets and extracting complex patterns.
- AI integration faces challenges like computational constraints and potential biases in AI systems.
- AI has the potential to optimize cancer registration efficiency and support cancer research and control.

## Abstract

Cancer has become the second leading cause of death, the global cancer burden is rapidly increasing, and there are marked disparities between and within countries worldwide. Population-based cancer registries systematically collect data on cancer patients in defined populations, which play a crucial role in planning and assessing cancer prevention and control strategies. While the development of cancer registration has been marked by increasing standardization of definitions and methods and the electronic processing of data, the advent of artificial intelligence (AI) offers opportunities to further reduce the labor-intensive nature of registry operations, particularly where registry resources are scarce. These include enabling the processing of large datasets, extracting complex or unstructured data patterns to support cancer registration data abstraction, and facilitating data quality and control. The analysis and dissemination of registry data are also increasingly integrating AI methodologies. This paper provides a comprehensive overview of the application of AI in cancer registration. We investigate the challenges associated with integrating AI into existing cancer registry structures, with a particular emphasis on network and computational constraints, uneven resource allocation, and potential biases and limitations within AI systems. We propose a forward-looking AI-enhanced framework for cancer registration, highlighting AI’s potential to optimize efficiency in cancer registration and the use of registry data for cancer control and cancer research.