E³ 520 - Artificial intelligence and clinical decision support

Lectures

1
E³ 520 - A. Clinical decision support workflow improved by artificial intelligence (AI)

E³ 520 - A. Clinical decision support workflow improved by artificial intelligence (AI)

13:29E. R. Ranschaert, Tilburg / NL

Learning Objectives 1. To learn how 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 520 - B. Data mining and machine learning for integrated clinical decision support

E³ 520 - B. Data mining and machine learning for integrated clinical decision support

17:18G. Boland, Boston, MA / US

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 520 - C. AI to predict treatment response

E³ 520 - C. AI to predict treatment response

15:30N. M. 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 520-1
Introduction by the moderator
E³ 520-2
A. Clinical decision support workflow improved by artificial intelligence (AI)
Learning Objectives
1. To learn how 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 520-3
B. Data mining and machine learning for integrated clinical decision support
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 520-4
C. AI to predict treatment response
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.
Live Q&A

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