Augmenting Health Data Using AI
The SAVE DATA project – a consortium of MEDVIA, Vito and imec – invites you to Augmenting Health Data Using AI, a free seminar on the opportunities and challenges involved in implementing and sustaining AI applications to secure health data.
Praktische info:
georganiseerd door:
Leertraject
We know that AI can significantly enhance the collection and utilization of health data in a variety of ways, including in predictive analytics, remote monitoring, diagnostics and drug development. But there is a lot to consider when applying artificial intelligence to your data, including:
Data quality: High-quality, diverse datasets are essential
Privacy: Data is sensitive and subject to strict privacy regulations
Interoperability: Data is often siloed across different systems and formats
Collaboration: AI developers need to collaborate closely with healthcare professionals
Join SAVE DATA at our upcoming seminar to hear about these challenges and potential solutions.
Program
12.00-13.00 Registration & lunch
13.00-14.00 Starting with AI: Overcoming barriers to implementation
– Willemien Laenens, Knowledge Centre Data & Society / AI Blind Spots in Healthcare
Introducing a card set that helps care professionals avoid replicating societal biases and structural inequalities
in the design, development and implementation of AI systems
– Gokhan Ertaylan, Vito / From Lab to Clinic: Accelerating AI’s Journey in Healthcare
The transformative journey of AI in healthcare, from initial conception to full clinical deployment
14.00-15.20 Applying AI best practices
– Lorin Werthen-Brabants, imec / Trustworthy ML for Healthcare: Challenges and Developments
Key
challenges to ensuring TML in healthcare settings, such as data
privacy, algorithmic bias and model interpretability, as well as recent
strategies to overcome these challenges
– Evelyn Verlinde, Leadlife
There will be a 20-minute break at 14.30
15.20-16.30 Ensuring continuity of AI applications
– Katrien Verbert, KU Leuven / Bridging the Gap: Human-Centered Explainable AI (XAI) in Healthcare
Explainability
methods that are tailored to the needs of healthcare professionals and
patients, as well as the results of user studies that investigate how
such explanations interact with personal characteristics, such as
expertise and need for cognition
– Jonas De Vylder, Barco / Keeping AI-powered Medical Devices Safe and Effective
Regulatory
hurdles of AI-powered medical devices, including AI’s ability to learn
and adapt and methods to ensure that AI-based tools reach clinicians
wihtout compromising patient well-being
16.30-17.30 Networking reception
Lesgevers / sprekers
Willemien Laenens
Willemien Laenens is project- en communicatiemedewerker bij het Kenniscentrum Data & Maatschappij. Het Kenniscentrum is de centrale hub voor maatschappelijke, juridische en ethische aspecten van datagedreven toepassingen en AI. Ze ondersteunt organisaties in het praktiseren van ethische waarden in innovatieprojecten, en focust hierbij onder meer op het betrekken van werknemers bij innovatieprocessen.
Lorin Werthen-Brabants
Postdoctoral Researcher at imec
Katrien Verbert
Katrien Verbert is professor at the Augment research group of KU Leuven. She obtained a doctoral degree in Computer Science in 2008 at KU Leuven, Belgium. She was a postdoctoral researcher of the Research Foundation – Flanders (FWO) at KU Leuven. She was an Assistant Professor at TU Eindhoven, the Netherlands (2013 –2014) and Vrije Universiteit Brussel, Belgium (2014 – 2015). Her research interests include visualization techniques, recommender systems, explainable AI, and visual analytics. She has been involved in several European and Flemish projects on these topics, including the EU ROLE, STELLAR, STELA, ABLE, LALA, PERSFO, Smart Tags and BigDataGrapes projects.
Gerelateerde opleidingen
