Refresher Course: Imaging Informatics / Artificial Intelligence and Machine Learning
RC 605 - Quality control and post-market surveillance of AI medical software
Lectures
1
Chairperson's introduction
05:00Charlotte L. Brouwer, Groningen / NL
2
Post-market surveillance of a software as a medical device
15:00Kicky Gerhilde van Leeuwen, De Bilt / NL
3
How to confirm the effectiveness and safety of the use of an AI medical software?
15:00Emilia Niemiec, Copenhagen / DK
4
An example of quality control of AI applications in imaging
15:00Federica Zanca, Leuven / BE
5
Panel discussion: How to implement quality control and post-market surveillance of AI tools in daily practice?
10:00Panel discussion: How to implement quality control and post-market surveillance of AI tools in daily practice?
5 min
Chairperson's introduction
Charlotte L. Brouwer, Groningen / Netherlands
15 min
Post-market surveillance of a software as a medical device
Kicky Gerhilde van Leeuwen, De Bilt / Netherlands
1. To learn about the need for post-market surveillance.
2. To understand how to perform post-market surveillance on AI applications.
3. To learn how to establish a fruitful industry-hospital collaboration for post-market surveillance of AI tools.
2. To understand how to perform post-market surveillance on AI applications.
3. To learn how to establish a fruitful industry-hospital collaboration for post-market surveillance of AI tools.
15 min
How to confirm the effectiveness and safety of the use of an AI medical software?
Emilia Niemiec, Copenhagen / Denmark
1. To learn how the safety and performance of an AI software are defined following the MDR.
2. To understand how to identify key performance indicators to assess safety and performance in clinical settings.
3. To review examples of safety and performance metrics of clinically in-use AI tools.
2. To understand how to identify key performance indicators to assess safety and performance in clinical settings.
3. To review examples of safety and performance metrics of clinically in-use AI tools.
15 min
An example of quality control of AI applications in imaging
Federica Zanca, Leuven / Belgium
1. To learn about a possible approach for real-time tracking of AI applications.
2. To learn about operational and clinical key performance indicators for tracking selected AI applications.
3. To learn about typical errors related to AI applications implemented in clinical settings.
2. To learn about operational and clinical key performance indicators for tracking selected AI applications.
3. To learn about typical errors related to AI applications implemented in clinical settings.
10 min
Panel discussion: How to implement quality control and post-market surveillance of AI tools in daily practice?