Powered by
02:13 CET
SF 8d - Artificial intelligence and radiology: a perfect match?
Imaging Informatics Artificial Intelligence & Machine Learning
Thursday, March 1, 16:00 - 17:30
Room: B
Moderators: A. K. Dixon (Cambridge/GB), W. Kim (Los Angeles/US)

A-984
16:00
Chairpersons' introduction (part 1)
A. K. Dixon; Cambridge/GB
Learning Objectives

1. To understand the role AI plays in radiology today and in the future.
2. To learn how AI can benefit radiologists.
3. To appreciate the benefits of AI for patients.

Abstract

With so much media attention and many startups focused on artificial intelligence in radiology, it can be easy for radiologists to get caught in the fear and hype surrounding this disruptive technology. What is the current state of AI in radiology - both its capabilities and limitations? What is hype vs. reality when it comes to using this technology in our field? It is the goal of this session to better understand the role AI plays in radiology today and in the near future through various AI experts in medical imaging. We will explore the use of AI in image interpretation and beyond into other aspects of radiological practice. By appreciating how this technology can benefit both radiologists and our patients, we hope to learn how we can use AI to augment radiologists.

A-985
16:03
Chairpersons' introduction (part 2)
W. Kim; Los Angeles/US
Learning Objectives

1. To understand the role AI plays in radiology today and in the future.
2. To learn how AI can benefit radiologists.
3. To appreciate the benefits of AI for patients.

Abstract

With so much media attention and many startups focused on artificial intelligence in radiology, it can be easy for radiologists to get caught in the fear and hype surrounding this disruptive technology. What is the current state of AI in radiology - both its capabilities and limitations? What is hype vs. reality when it comes to using this technology in our field? It is the goal of this session to better understand the role AI plays in radiology today and in the near future through various AI experts in medical imaging. We will explore the use of AI in image interpretation and beyond into other aspects of radiological practice. By appreciating how this technology can benefit both radiologists and our patients, we hope to learn how we can use AI to augment radiologists.

A-986
16:05
An overview of where artificial intelligence could/will take us in radiology
M. Forsting; Essen/DE
Learning Objectives

1. To learn what the prerequisite of application of AI in medicine is.
2. To understand the difference between AI and radiomics.
3. To learn that radiology will grow with AI.
4. To appreciate that AI will be a topic not only for radiology.

A-987
16:23
The science of artificial intelligence and machine learning: creating a partnership between radiologist and machine
C. Langlotz; Stanford/US
Learning Objectives

1. To learn about origins of artificial intelligence/deep learning and how they will change radiological practice.
2. To appreciate important AI questions: analysis of radiology free-text reports and decision support.
3. To review clinically relevant AI applications and their likely effect on global radiology practice.
4. To understand the challenges for AI in radiology and the reasons why AI will not replace radiologists.

A-988
16:41
How artificial intelligence can already contribute to fully automatic, multi-organ segmentation, and registration
B. Glocker; London/GB
Learning Objectives

1. To learn how deep learning can be employed to automatically segment anatomical structures from medical images.
2. To appreciate that it takes considerable effort and time to carefully prepare training data.
3. To understand what can be expected from AI and what the limitations of today's methods are.

A-989
16:59
Artificial intelligence and radiology: a perfect match. Radiology and radiologists: a painful divorce?
B. van Ginneken; Nijmegen/NL
Learning Objectives

  1. To learn about different types of deep learning products and services which analyse radiological images.
  2. To appreciate which parts of the work of radiologist can (and will not) be automated in the near future
  3. To review clinically relevant AI applications currently available and those not yet on the market.
  4. To understand that AI and radiology may be a perfect match, but perhaps not for radiologists.

17:17
Panel discussion: So where does AI go from here?
<
This website uses cookies. Learn more