President Update in Medical Imaging, Radiologist at Damiaan General Hospital, Ostend
(Ostend, BE)
AI in Medical Imaging Conference
16 September 2022
Bruges Meeting & Conference Centre,
Bruges, Belgium
What you will learn:
Are you thinking about adopting AI in your hospital, screening programme, or radiology team? This conference will help you understand the current use cases for medical imaging AI, evaluate commercial vendors, and implement the solutions that fit your needs and directly benefit your patients. Recognised speakers will share their expertise with AI-based tools in clinical practice and answer your questions.
CME accreditation is offered, as well as a certificate of attendance
Why should you attend this conference?
- Distinguished and rewarded international faculty
- Close interaction with international attendees and faculty
- Refreshing content providing new perspectives
- State-of-the-art lectures
- Hot topic tutorial lectures
- CME credits are being sought for by the European Council for Continuous Medical Education
Scientific Faculty
Professor of Medical Image Analysis at Radboud University Medical Center
(Nijmegen, NL)
President of The European Institute for Health Records and the European Institute for Innovation through Health Data
(London, UK)
Key Opinion Leader in AI for Radiology, Digital Doctor at ETZ Hospital, Visiting Professor of Radiology at Ghent University, EuSoMII Past President
(Ghent, BE)
Radiation Oncologist Haga Hospital, CatalyzIT
(Den Haag, NL)
Head of Interventional Radiology Unit, Coronary Heart Disease Vendée
(Nantes, FR)
Radiologist at Algemeen Ziekenhuis Klina V.Z.W. & Antwerp University Hospital
(Antwerp, BE)
Radiologist at the Netherlands Cancer Institute
(Amsterdam, NL)
Instructor at Stanford Radiology, Cofounder at Segmed (YC W20), Jr Deputy Editor at European Radiology
(Stanford, US)
DPO & Information Governance Lead for The European Institute for Innovation through Health Data, Senior Research Fellow UCL
(London, UK)
Topics
- The implementation of AI in medical imaging
- AI in the lung cancer pathway
- The evolution of the AI market (vendors, market places, platforms)
- Financial hurdles in implementing AI
- Data protection, impact assessment and data sharing
- Rethinking radiology
- Risks of using AI: over-reliance and automation bias
- How to monitor AI applications?
- AI in oncologic imaging: real-world applications
- Medical imaging data curation for machine learning
- AI in breast imaging
- Ethics and governance using AI in medical imaging
- Federated learning and data privacy for medical imaging
- The data-driven doctor