Judy Wawira Gichoya

female portrait
Judy
Gichoya
M.D.
Data and Technology Advancement (DATA) National Service Scholar

Democracy 2, Suite 200

6707 Democracy Blvd.

Bethesda, MD 20817

Biography

Dr. Gichoya is a multidisciplinary researcher, trained as both an informatician and an Interventional radiologist. She is an assistant professor at Emory university in Interventional Radiology and Informatics. She comes to the NIH via the DATA scholars program where she serves as the liaison between NIBIB and the Fogarty International Center to help with the Open Data Science Platform (OSDP) component of the DSI Africa Initiative to “Harness Data Science for Health In Africa.” Drawing upon extensive experience with open source communities and contextual knowledge in Africa, she hopes to leverage her skills to build capacity for data science in Africa. Dr. Gichoya is a member of the Cancer Prevention and Control Research Program at Winship Cancer Institute. She holds professional memberships with Radiological Society of North America, American College of Radiology, Society of Interventional Radiology, Society of Imaging Informatics in Medicine and American Medical Informatics Association.

Dr. Gichoya earned her Medical Degree from Moi University in Kenya. She completed her medical internship at Kiambu District Hospital. She earned a Masters of Science in Health Informatics from Indiana University Purdue University in Indianapolis, Indiana. In addition, she completed post-doctoral training in informatics at Regenstrief Institute in Indianapolis, Indiana, and a residency in diagnostic radiology at Indiana University. Prior to arriving at Emory, she completed a fellowship in interventional radiology at Oregon Health Sciences University in Portland, Oregon.

Dr. Gichoya was recognized as the 2021 Most Influential Radiology Researcher by the radiology community website AuntMinnie.com.

Research Interests

Dr. Gichoya’s research interests include studying clinical disparities for minimally invasive procedures, validating machine learning models for health in real clinical settings, exploring explainability, fairness, and a specific focus on how algorithms fail. She has worked on the curation of datasets for the SIIM (Society for Imaging Informatics in Medicine) hackathon and ML committee. She volunteers on the ACR and RSNA machine learning committees to support the AI ecosystem to advance development and use of AI in medicine. She is currently working on the sociotechnical context for AI explainability for radiology, especially the dimensions of human factors that govern user perceptions and preferences of XAI systems. She has been funded through the Grand Challenges Canada, NIBIB and NSF ECCS.