EUROPEAN SCHOOL OF RADIOLOGY

Intelligence. Innovation. Imaging.

Lectures

1
Impact of AI in continuous education in radiology

Impact of AI in continuous education in radiology

18:08E. Kotter

2
Impact of AI in the management of the Radiology Department: a Leadership challenge

Impact of AI in the management of the Radiology Department: a Leadership challenge

23:23C. Becker

3
The future of radiology and radiologists

The future of radiology and radiologists

58:48

4
Who is responsible for the diagnosis? Man or machine?

Who is responsible for the diagnosis? Man or machine?

13:20M. Zins

5
Who controls patient data in the era of AI? Man or machine?

Who controls patient data in the era of AI? Man or machine?

18:36P. van Ooijen

6
Making the shift to value based healthcare

Making the shift to value based healthcare

19:17J. Visser

7
Contextflow

Contextflow

12:42

8
Icometrix

Icometrix

13:46

9
Incepto

Incepto

14:05

10
Infervision

Infervision

11:35

11
Median

Median

11:54

12
Quibim

Quibim

19:23

13
Terarecon

Terarecon

14:57

14
Welcome and introduction

Welcome and introduction

02:29V. Vilgrain

15
Welcome and introduction

Welcome and introduction

01:18E. Neri

16
Artificial intelligence. The view of the computer scientist

Artificial intelligence. The view of the computer scientist

19:50J. Bishop

17
Machine and deep learning in radiology

Machine and deep learning in radiology

40:20M. De Bruijne

18
Imaging biomarkers and radiomics: source of big data for AI

Imaging biomarkers and radiomics: source of big data for AI

24:01L. Marti-Bonmati

19
From raw data to beautiful images

From raw data to beautiful images

20:18D. Rueckert

20
Neuro

Neuro

16:06P. Parizel

21
Chest

Chest

15:01H. Kauczor

22
Cardiovascular

Cardiovascular

23:37M. Francone

23
Abdomen and GI tract

Abdomen and GI tract

16:16D. Regge

24
Breast

Breast

14:41A. Gubern-Mérida

25
Ethics and responsibilities in AI

Ethics and responsibilities in AI

20:18A. Brady

26
ESR Paper on Artificial Intelligence in Radiology

ESR Paper on Artificial Intelligence in Radiology

14:28E. Neri

27
Integration of AI in the imaging workflow

Integration of AI in the imaging workflow

21:08E. Ranschaert

28
Impact of AI in teaching radiology

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:

  1. to learn the basic principles of machine and deep learning
  2. to forecast the professional impact of AI in radiology
  3. to explore the potential and emerging clinical application
  4. to measure the radiologist’s performance in image interpretation with respect to AI tools

PEP Subscription Required

This course is only accessible for ESR Premium Education Package subscribers.