Title : Bridging the gap: Leveraging AI and GIS to unmask disparities in women's cancer care
Abstract:
This presentation explores the transformative potential of Machine Learning (ML) and Artificial Intelligence (AI) integrated with Geographic Information Systems (GIS) to address health disparities in women's cancer care. We investigate how these technologies can illuminate the interplay between geographic patterns, socioeconomic factors, and environmental exposures contributing to unequal cancer outcomes.
Healthcare disparities in women's cancer care persist. This study introduces the Intelligent Catchment Analysis Tool (iCAT), an innovative platform leveraging Artificial Intelligence (AI) and Machine Learning (ML) within a Geographic Information System (GIS) framework. iCAT tackles these disparities by analyzing vast datasets encompassing demographics, healthcare access, cancer incidence rates, and environmental factors. By integrating with GIS, iCAT facilitates the identification of geographic hotspots with heightened cancer risk and limited healthcare resources.
The platform empowers users, including patients, providers, and community organizations, to query diverse data and participate in the evaluation process. This collaborative approach ensures iCAT remains responsive to local needs and informs tailored interventions for impactful outcomes. The research showcases iCAT's capabilities through two case studies that analyze social and environmental factors influencing women's cancer care disparities.
By empowering communities with data-driven insights, iCAT fosters informed decision-making for optimized resource allocation and improved healthcare equity in women's cancer care. This project demonstrates the transformative potential of AI and GIS integration in advancing our understanding of disparities and ultimately promoting equitable access to quality care for all women.
Audience Take Away:
- Understanding the Intersection of Technology and Women's Health: Attendees will gain insight into how advanced technologies such as Machine Learning (ML) and Artificial Intelligence (AI) intersect with Geographic Information Systems (GIS) to unravel health disparities in women's cancer care. They will learn about the transformative potential of these tools in analyzing complex datasets and identifying root causes of inequalities.
- Practical Applications of GIS-Based Analysis: The presentation will provide useful examples of how GIS-based analysis, augmented by ML and AI, can be applied to map healthcare outcomes and disease disparities. Attendees will learn how to use these tools to query diverse datasets, including public health data, healthcare utilization metrics, and treatment records, to gain actionable insights into gaps and disparities in diagnosis and treatment modalities.
- Promoting Evidence-Based Interventions: Attendees will discover how ML and AI insights can inform targeted interventions and policy initiatives to mitigate health disparities in women's cancer care. They will learn how to develop tailored strategies to enhance access to screening, diagnostic services, and treatment options for underserved populations based on the findings from GIS-based analyses.
- Collaborative Opportunities for Research and Teaching: The research presented offers valuable insights and methodologies that other faculty members can leverage to expand their study or incorporate into their teaching curriculum. By engaging in interdisciplinary collaborations and adopting innovative approaches, faculty can enhance their research endeavors and equip students with practical skills in health analytics and GIS-based analysis.
- Overall, this presentation provides a practical solution to the complex problem of health disparities in women's cancer care by leveraging advanced technologies and interdisciplinary approaches. It empowers attendees to utilize ML, AI, and GIS tools to improve the accuracy of healthcare analyses, inform evidence-based interventions, and ultimately promote equitable access to quality cancer care for all women.
