ArticleScientific reports2024
Utilizing geospatial artificial intelligence to map cancer disparities across health regions.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- [Pathology data in spatial epidemiology (REDPath) : A web-based application for oncology and service planning].Pathologie (Heidelberg, Germany) · 2026Article
- Review
- The Practical, Robust Implementation and Sustainability (PRISM)-capabilities model for use of Artificial Intelligence in community-engaged implementation science research.Implementation science : IS · 2025Article
- Integrating Artificial Intelligence Into Cancer Care: Enhancing Nursing Practice and Bridging Disparities.Clinical journal of oncology nursing · 2025Article
- A Geospatial Analysis of the Lung Cancer Burden in Philadelphia, Using Pennsylvania Cancer Registry Data from 2008-2017.International journal of environmental research and public health · 2025Article
- The use of geographic information systems (GIS) in studying mental health service delivery: A scoping review.Global mental health (Cambridge, England) · 2025Review
- Translation of Epigenetics in Cell-Free DNA Liquid Biopsy Technology and Precision Oncology.Current issues in molecular biology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We have developed an innovative tool, the Intelligent Catchment Analysis Tool (iCAT), designed to identify and address healthcare disparities across specific regions. Powered by Artificial Intelligence and Machine Learning, our tool employs a robust Geographic Information System (GIS) to map healthcare outcomes and disease disparities. iCAT allows users to query publicly available data sources, health system data, and treatment data, offering insights into gaps and disparities in diagnosis and treatment paradigms. This project aims to promote best practices to bridge the gap in healthcare access, resources, education, and economic opportunities. The project aims to engage local and regional stakeholders in data collection and evaluation, including patients, providers, and organizations. Their active involvement helps refine the platform and guides targeted interventions for more effective outcomes. In this paper, we present two sample illustrations demonstrating how iCAT identifies healthcare disparities and analyzes the impact of social and environmental variables on outcomes. Over time, this platform can help communities make decisions to optimize resource allocation.
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.