Speaker Profile

Ph.D., Founder & CEO, QUIBIM

Angel Alberich-Bayarri is a bioengineer, researcher and entrepreneur in medical image processing and the application of AI methods to radiology. He has experience in the lab-to-market translation of AI models covering unmet clinical needs in neurology, respiratory diseases, musculoskeletal and oncology scenarios. He is author of more than 60 publications in the field of imaging biomarkers, author of more than 15 book chapters and editor of 2 books. He is board member of several scientific societies and panels, such as the European Society of Medical Imaging Informatics (EUSOMII) and the European Imaging Biomarkers Alliance (EIBALL). In March 2020, he was one of the main promoters of the Imaging COVID-19 AI initiative, which currently hosts the biggest worldwide database of COVID-19 CT examinations and is leading the development of a highly accurate model for disease severity scoring and in-patients follow-up. He was named one of MIT Technology Review’s 35 most innovative people in the world under age 35, in 2015.

AI & Data Science Showcase:

QUIBIM provides an AI platform focused on converting medical images into meaningful data. The company has a portfolio of accurate quantitative imaging solutions across neurology, lung, musculoskeletal and oncology disease areas.

AI and Medical Imaging for Innovation in COVID-19
The presentation will review the main challenges in the field of AI in medical imaging in the most relevant disease scenarios as well as the need for accelerating the lab-to-market transition of tools that can support COVID-19 patients management during the pandemic situation.

 Session Abstract – PMWC 2022 Silicon Valley

The PMWC 2022 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.