Abstract 1049: Advancing health equity in oncology through AI, GIS, and data-driven tools

Abstract Cancer outcomes are influenced by geographic, socioeconomic, and environmental factors, especially in rural and underserved urban areas. Such regions often lack appropriate screening and specialized care, leading to late-stage diagnoses and poor overall outcomes. Key socioeconomic determinants influencing disparities in cancer care include income, education, environmental conditions, insurance status, and geographic proximity to qualified healthcare providers. To address these inequities, sophisticated tools are needed to identify high-risk areas and to improve resource allocation. To address this need, we developed the Intelligent Cancer Catchment Area Tool (iCAT), a GIS-enabled platform enhanced by artificial intelligence (AI). iCAT integrates public health sources and health systems data to generate a comprehensive, geographically diverse analysis of cancer disparities. On this platform, mapping capabilities are analyzed with advanced statistics and machine learning algorithms to derive actionable insights into cancer social and environmental drivers of gaps in healthcare. Two recent module additions to iCAT are 1. integration of authoritative datasets, including SEER, CDC, and other trusted sources, and 2. advanced preprocessing, in which LLMs are used to clean data and handle confounders. We have improved the analytics package to enable univariate and multivariate analyses to find more complex relationships among the variables. We are adding AI-assisted literature matching to help formulate and suggest evidence-based research questions. Future directions for iCAT include quantum-enhanced predictive modeling, real-time analytics with biometric data from wearable devices, and hyper-localized risk assessment using augmented reality visualizations. We also plan to incorporate blockchain for data security and AI-driven optimization algorithms that guarantee privacy while enhancing intervention planning. We are developing iCAT as a tool for healthcare providers, policymakers, and researchers to make evidence-based decisions. With further development, we anticipate use cases ranging from deploying mobile screening units to culturally adapted educational programs and forming policy reforms. This new approach is a key milestone toward eliminating health disparities and redefinition of equitable cancer care." Citation Format: Ahmed Fadiel, Saleh Fadel, W. Butt, K.D. Eichenbaum, M. Abbasi, A. Elbialy, Kunle Odunsi. Advancing health equity in oncology through AI, GIS, and data-driven tools [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1049.