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Hari Trivedi
Assistant Professor of Radiology and Biomedical Informatics at Emory University
Dr. Hari Trivedi is an Assistant Professor in the Department of Radiology with a joint appointment in the Department of Biomedical Informatics at Emory University School of Medicine.12 He joined Emory University in October 2018 and has since been focusing on applying machine learning to solve problems in radiology.3
Background and Education
Dr. Trivedi received his MD from the Medical College of Georgia in Augusta, Georgia. He completed his residency in diagnostic radiology and a fellowship in musculoskeletal imaging at the University of California, San Francisco (UCSF).23
Clinical Expertise
His clinical expertise is in emergency and trauma imaging, with additional fellowship training in musculoskeletal imaging.12 Dr. Trivedi began practicing in 2018.1
Research and Interests
Dr. Trivedi's research focuses on the application of machine learning (artificial intelligence) to address challenges in radiology. Some of his recent work includes:
- Developing algorithms to improve breast cancer screening12
- Applying deep learning and natural language processing to radiology3
- Exploring image analysis and data analytics in healthcare3
He is the co-director of the Health Innovation and Translational Informatics (HITI) lab at Emory University, where he supervises numerous students working on machine learning projects related to radiology.23
Professional Memberships and Roles
Dr. Trivedi holds memberships in several professional organizations, including:
- Radiological Society of North America
- Society for Skeletal Radiology
- American College of Radiology2
Additionally, he serves as a consultant for various healthcare and technology companies, including Solis Mammography, Flatiron Health, and PMX.3
Entrepreneurship
In April 2019, Dr. Trivedi founded Lightbox AI, a company focused on physician-driven annotation and curation for Radiology AI.3
Dr. Trivedi's work exemplifies the intersection of medicine, technology, and research, contributing to advancements in radiology and medical imaging through the application of artificial intelligence and machine learning techniques.
