Florence Xini Doo, MD

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 a clinical abdominal radiologist, Assistant Professor in the Dept of Radiology and Nuclear Medicine, Faculty at the University of Maryland-Institute for Health Computing (UM-IHC), and Director of Innovation at the University of Maryland Medical Intelligent Imaging (UM2ii) Center. 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 the RSNA Roentgen Resident/Fellow Research Award and Alpha Omega Alpha (AOA). 

Dr. Doo has published 30 peer-reviewed articles, and is currently grant-funded by AAR CERRAF (a foundation career development award), the UMMC Innovation Challenge award for a clinical LLM tool, a MACCHE (Johns Hopkins NIH MHHD P50 Center) project grant, and the internal UMB ICTR/CTSA K12 mentored career development grant. 

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.

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

https://orcid.org/0000-0001-6519-5222