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Florence Xini Doo, MD

Academic Title:

Assistant Professor

Primary Appointment:

Diagnostic Radiology and Nuclear Medicine

Additional Title:

Director of Innovation, University of Maryland Medical Intelligent Imaging (UM2ii) Center

Education and Training

2006–2010      B.A., Neuroscience, Wellesley College    
2010–2012       M.A., Graduate Medical Sciences, Boston University
2013–2017       M.D., Oakland University William Beaumont School of Medicine 

Post-Graduate Education and Training                                                                                   

2017–2018       Preliminary-Medicine Residency, Yale-Waterbury Hospital
2018–2022       Diagnostic Radiology Residency, Mt. Sinai West
2022–2023       Body Imaging Fellowship, Stanford University 
2022–2023       Informatics Fellowship, American College of Radiology


Florence (Flo) Doo, M.D. is Director of Innovation at the University of Maryland Medical Intelligent Imaging (UM2ii) Center, and Assistant Professor in the Dept of Radiology and Nuclear Medicine. She completed her radiology residency at Mt Sinai West, and dual fellowships in Body Imaging at Stanford University, and the nationally-selected Informatics fellowship through the American College of Radiology (ACR).

Dr. Doo's expertise spans the domains of clinical radiology (body/abdominal imaging), artificial intelligence (AI)/informatics, and business/entrepreneurship. She has held leadership positions in various local and national medical organizations, and has received numerous awards including Alpha Omega Alpha (AOA).  Dr. Doo has published over 20 peer-reviewed articles in several distinguished journals, and her research has been supported by the AUR GE Radiology Research Fellowship grant award.

As a physician innovator, her current research interests include translating technologies into clinical patient benefit, with a focus on sustainable AI and global health/climate change impacts.

Research/Clinical Keywords

abdominal imaging, artificial intelligence (AI), large language models (LLMs), deep learning, informatics, business, entrepreneurship, innovation, population health, sustainability, climate change

Highlighted Publications