ReviewScience bulletin2026
The role of artificial intelligence in advancing population-based cancer registration.
Review in Science bulletin, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
35 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.