Explainable AI for healthcare – challenges and future outlook
AI and machine learning have the potential to revolutionize health care. New tools can help medical doctors to more accurate diagnoses and wiser decisions on which treatments to start. This would be beneficial for both individual patients and the society as a whole – but a key issue is that doctors need to trust their AI-assistants. That is, the machine learning models need to be explainable so that clinicians understand where the advice is coming from.
Join us, as the Tech Tuesday seminar series returns in 2024, for a breakfast seminar hosted by DSV, the Department of Computer and Systems Sciences at Stockholm University.
During this talk, professor Panagiotis Papapetrou will give you an overview of state-of-the-art research on explainable machine learning models in healthcare applications.
The talk will cover:
– how to provide trustworthy explanations in the healthcare context
– the challenges of multimodality in healthcare data sources
– how to achieve good tradeoffs between data privacy, explainability, and model performance
Agenda for February 6
8.30 Breakfast and mingle at DSV, Nod building, Borgarfjordsgatan 12, room L30, 1st floor
9.00 Welcome by DSV and Kista Science City
9.05 Presentation by Panagiotis Papapetrou on “Explainable AI for healthcare – challenges and future outlook”
9.20 Q&A
9.30 The event ends
About the speaker
Panagiotis Papapetrou is a professor in data science and vice head of DSV, the Department of Computer and Systems Sciences. With over 200 employees and 5 000 students at Campus Kista, DSV is one of the biggest departments at Stockholm University. Panagiotis leads the Data Science Research Group.