Dr. Zhuo Chen is an Assistant Professor in the School of Medicine at Case Western Reserve University. With a comprehensive background in geography and geographic information science (GIScience), Dr. Chen specializes in advanced geospatial data processing, analysis, harmonization, and the application of machine learning and artificial intelligence to complex health problems. Driven by a commitment to advancing public health knowledge and informing healthcare policy, his research centers on health geography, focusing on health disparities and the environmental and socio-economic determinants of disease.
Dr. Chen leverages emerging big spatial data, such as high-resolution satellite imagery and street view images, coupled with sophisticated AI techniques like deep learning, to uncover novel insights into environmental risk factors and predict health outcomes, particularly in cardiovascular and cardiometabolic health. He leads the Spatial Health & Environmental Intelligence (SHEIL) Program, developing robust methodologies to investigate the interconnected links between environment and health.
Pushing the boundaries of spatial health research, Dr. Chen is also actively exploring and adapting cutting-edge Artificial Intelligence, including Large Language Models (LLMs) and multimodal Foundation Models within geospatial context. This work aims to create a deeper, more integrated understanding of spatial health dynamics by synthesizing diverse data streams – from imagery and sensor data to clinical text and social determinants. Through active collaborations with health researchers and clinicians, Dr. Chen focuses on translating these advanced research findings into actionable interventions that improve population health and promote health equity.
Research Information
Research Interests
Dr. Chen's research program centers on the intersection of geospatial science, artificial intelligence, environmental health, and population health. Key areas include:
- AI-Driven Environmental Exposure Assessment: Utilizing deep learning and computer vision with satellite and street-level imagery to quantify built environment features (road quality, green space, building conditions) and assess their impact on cardiovascular risk and health disparities.
- Geographic & Environmental Determinants of Health: Investigating how factors like air pollution, neighborhood walkability, healthcare accessibility, mobility, and historical factors like redlining influence cardiovascular health outcomes and contribute to health inequities.
- Spatial Data Science & Health Informatics: Developing and applying advanced geospatial modeling, spatial statistics, and machine learning techniques to analyze large-scale health and environmental datasets.
- Foundation Models for Spatial Health Intelligence: Exploring the use of Large Language Models (LLMs) and multimodal AI to integrate diverse data streams (imagery, text, structured data) for a more holistic understanding of place-based health determinants.
Research Awards
2022 – 2023 PI, Integrated Fine-Scale Cartographic-Enabled Socio-Environmental Phenotyping as Drivers of Cardiometabolic Risk, ACHIEVE GreatER (Addressing Cardiometabolic Health Inequities by Early PreVEntion in the Great LakEs Region) IDC Pilot Project, $40,000.
Publications
Selected Publications
Chen, Z., Dazard, J. E., Khalifa, Y., Motairek, I., Al-Kindi, S., & Rajagopalan, S. (2024). Artificial intelligence–based assessment of built environment from Google Street View and coronary artery disease prevalence. European Heart Journal, 45(17), 1540-1549.
Chen, Z., Dazard, J. E., Khalifa, Y., Motairek, I., Kreatsoulas, C., Rajagopalan, S., & Al-Kindi, S. (2024). Deep learning–based assessment of built environment from satellite images and cardiometabolic disease prevalence. JAMA cardiology, 9(6), 556-564.
Chen Z., Salerno R.V.O.P., Dazard JE, Sirasapalli SK, Makhlouf M., Motairek I, Moorthy S., Al-Kindi S, Rajagopalan S. (2024). AI-facilitated assessment of built environment using neighborhood satellite imagery and cardiovascular risk. Journal of the American College of Cardiology, 84(18), 1733-1744.
Chen, Z., Salerno, P. R., Dazard, J. E., Makhlouf, M. H., Deo, S., Rajagopalan, S., & Al-Kindi, S. (2025). Deep Learning Analysis of Google Street View to Assess Residential Built Environment and Cardiovascular Risk in a US Midwestern Retrospective Cohort. European Journal of Preventive Cardiology, zwaf038.
Chen, Z., Dazard, J. E., de Oliveira Salerno, P. R. V., Sirasapalli, S. K., Makhlouf, M. H., Rajagopalan, S., & Al-Kindi, S. (2025). Composite socio-environmental risk score for cardiovascular assessment: An explainable machine learning approach. American Journal of Preventive Cardiology, 100964.
(Note: For a complete list, please see Dr. Chen's Google Scholar Profile)