E³ - Advanced Courses

Artificial intelligence and clinical decision support

Lectures

1
E³ 522 - A. Clinical decision support workflow improved by artificial intelligence (AI)

E³ 522 - A. Clinical decision support workflow improved by artificial intelligence (AI)

19:36E. Ranschaert, Tilburg / Netherlands

Learning Objectives
1. To learn how a decision support workflow can be supported and improved by AI.
2. To understand the different workflow parts in which AI can play a role.
3. To discuss how to evaluate the clinical value of AI in decision support.

2
E³ 522 - B. Data mining and machine learning for integrated clinical decision support

E³ 522 - B. Data mining and machine learning for integrated clinical decision support

19:34G. Boland, Boston, MA / United States

Learning Objectives
1. To understand how data mining can help in clinical decision support.
2. To learn about the needs and limitations of standardisation for AI-assisted clinical decision support.
3. To learn about the state of the art in AI-assisted clinical decision support.

3
E³ 522 - C. AI to predict treatment response

E³ 522 - C. AI to predict treatment response

15:30N. deSouza, Sutton / UK

Learning Objectives
1. To understand the role of AI in moving towards precision medicine.
2. To understand the current potential of AI for monitoring response.
3. To understand how to manage AI in a clinical workflow as a decision support tool.

E³ 522-1
A. Clinical decision support workflow improved by artificial intelligence (AI)
Erik R. Ranschaert, Turnhout / Belgium
Learning Objectives
1. To learn how a decision support workflow can be supported and improved by AI.
2. To understand the different workflow parts in which AI can play a role.
3. To discuss how to evaluate the clinical value of AI in decision support.
E³ 522-2
B. Data mining and machine learning for integrated clinical decision support
Giles Boland, Wellesley / United States
Learning Objectives
1. To understand how data mining can help in clinical decision support.
2. To learn about the needs and limitations of standardisation for AI-assisted clinical decision support.
3. To learn about the state of the art in AI-assisted clinical decision support.
E³ 522-3
C. AI to predict treatment response
Nandita Desouza, London / United Kingdom
Learning Objectives
1. To understand the role of AI in moving towards precision medicine.
2. To understand the current potential of AI for monitoring response.
3. To understand how to manage AI in a clinical workflow as a decision support tool.