AIMI Research Meeting: Charting A Path Towards an Equitable Data Ecosystem - Alaa Youssef, PhD & Madelena Ng, DrPH, MPH
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Abstract: The use of Artificial Intelligence and Machine Learning (AI/ML) in healthcare has shown significant promise in improving clinical decision-making. Despite this potential, the limited availability of health data poses a major challenge to the equitable development of AI technologies in the healthcare sector. Our research involved a detailed study of 18 health organizations across academia, government, non-profit, and industry sectors, focusing on their practices in sharing clinical data. The aim of this talk is to discuss the key organizational factors that influence how these organizations share clinical data with AI/ML developers. By exploring these factors, our findings provide insights into the current state of data sharing in healthcare and suggest ways to enhance access to vital health data for AI-driven solutions in healthcare.
Postdoctoral Fellow
Stanford University
About: Dr. Youssef is a postdoctoral fellow at the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), in the Department of Radiology. She received her Doctor of Philosophy (PhD) degree in Population Health and Medical Education from the Institute of Medical Science, University of Toronto in 2021, Canada. Her research lies at the intersection of AI ethics and safety. Her work involves understanding the barriers to AI adoption within clinical workflows, with a particular focus on the ethical considerations that arise during AI development and deployment. As an educator, Dr. Youssef is committed to designing AI education and research opportunities that promote a diverse and equitable workforce in machine learning and healthcare, addressing the challenges of bias and ensuring inclusivity in the field.
Postdoctoral Scholar
Stanford University
About: Madelena is a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research (BMIR). Her research aims to illuminate the evolving ethical and practical challenges among digital and emerging technologies (e.g., web- and app-based population health research, clinical AI solutions, blockchain for health data). Her work in the Boussard Lab focuses on discerning key factors for clinical AI solutions to flourish in practice—from the readiness of the datasets for machine learning research to the operational principles that are required for successful clinical deployment.
Attendance is open to the Stanford community. If you would like to attend in-person or on Zoom, please contact the AIMI Center at aimicenter@stanford.edu.