Dr. Gloria Hyunjung Kwak is an Assistant Professor at the Emory University Nursing School and a member of the Centre for Data Science. Her research focuses on the development of equitable AI in healthcare, with a particular emphasis on ensuring fairness in AI-driven clinical decision-making. She aims to bridge the gap between advanced AI techniques and real-world healthcare applications, promoting more just and inclusive healthcare solutions.

Septic Shock Requiring Three Vasopressors: Patient Demographics and Outcomes
Gloria Hyunjung Kwak, RWMA Madushani, Lasith Adhikari, April Yujie Yan, Eric S Rosenthal, Kahina Sebbane, Zahia Yanes, David Restrepo, Adrian Wong, Adrian, Leo Anthony Celi, and Emmett Kistler
Critical Care Explorations, 2024

Social Determinants of Health and Limitation of Life-Sustaining Therapy in Neurocritical Care: A CHoRUS Pilot Project
Gloria Hyunjung Kwak, Hera A Kamdar, Molly J Douglas, Hui Hu, Sophie E Ack, India A Lissak, Andrew E Williams, Nirupama Yechoor, and Eric S Rosenthal
Neurocritical Care, 2024

Health Beyond Clinical Data:How Social Determinants of Health Impact Health Predictions Ming Yang, Gloria Hyunjung Kwak, Tom Pollard, Leo Anthony Celi, and Marzyeh GhassemiIn AAAI/ACM Conference on AI, Ethics, and Society, 2023

My teaching philosophy centers on integrating practical, real-world applications of AI in healthcare, drawing from my academic background and industrial experience. I strive to cultivate a collaborative learning environment where students actively engage with real-world cases, seamlessly connecting theoretical concepts with practical implementation. While emphasizing the importance of fairness and ethical considerations in AI, my primary focus is on equipping students with the skills to effectively apply AI solutions in healthcare settings. I encourage teamwork, cross-disciplinary collaboration, and critical thinking, preparing students to tackle complex challenges and innovate in the evolving landscape of healthcare.

My research is dedicated to enhancing fairness in AI applications within healthcare, with a focus on precision medicine in critical care, disability patient groups, and socially vulnerable populations. By examining treatment pattern differences across hospitals, I aim to uncover and address disparities in care delivery. Leveraging cross-modal machine learning techniques, my work seeks to develop AI models that are not only accurate but also equitable, ensuring that these technologies benefit diverse patient populations rather than perpetuate existing biases.

More information about her work and teaching approach can be found on her website: http://eq-ai-health.org/.