EUROPEAN SCHOOL OF RADIOLOGY
Intelligence. Innovation. Imaging.
Lectures
Impact of AI in continuous education in radiology
18:08E. Kotter
Impact of AI in the management of the Radiology Department: a Leadership challenge
23:23C. Becker
The future of radiology and radiologists
58:48
Who is responsible for the diagnosis? Man or machine?
13:20M. Zins
Who controls patient data in the era of AI? Man or machine?
18:36P. van Ooijen
Making the shift to value based healthcare
19:17J. Visser
Contextflow
12:42
Icometrix
13:46
Incepto
14:05
Infervision
11:35
Median
11:54
Quibim
19:23
Terarecon
14:57
Welcome and introduction
02:29V. Vilgrain
Welcome and introduction
01:18E. Neri
Artificial intelligence. The view of the computer scientist
19:50J. Bishop
Machine and deep learning in radiology
40:20M. De Bruijne
Imaging biomarkers and radiomics: source of big data for AI
24:01L. Marti-Bonmati
From raw data to beautiful images
20:18D. Rueckert
Neuro
16:06P. Parizel
Chest
15:01H. Kauczor
Cardiovascular
23:37M. Francone
Abdomen and GI tract
16:16D. Regge
Breast
14:41A. Gubern-Mérida
Ethics and responsibilities in AI
20:18A. Brady
ESR Paper on Artificial Intelligence in Radiology
14:28E. Neri
Integration of AI in the imaging workflow
21:08E. Ranschaert
Impact of AI in teaching radiology
19:09J. Sosna
The main focuses of this ESR/ESOR AI event are the basic technical principles of AI and how they are applied to diagnostic imaging and clinical applications. The basic concepts of machine and deep learning will be explored, more information about the type and consistency of imaging data processed by AI tools will be unveiled, and the main potential and emerging clinical applications of this technology will also be covered. The event features two special sessions which will bring a European flavour to it. The first of these sessions is a panel discussion about the future of radiology and radiologists in the era of AI. The second session is open to SME companies in the field of AI to present themselves, showcasing their product and the advantages of AI in different scenarios of their choice.
Learning Objectives:
- to learn the basic principles of machine and deep learning
- to forecast the professional impact of AI in radiology
- to explore the potential and emerging clinical application
- to measure the radiologist’s performance in image interpretation with respect to AI tools