In a world where our smartphones and wearables are gathering overwhelming amounts of data, how can we make use of them to better understand our health? This question is explored by our guest Dr. Mark Hoogendoorn, a professor of artificial intelligence at the Vrije Universiteit Amsterdam. An expert on machine learning, Mark discusses how this analytical method can be used in healthcare and the challenges that come with all this data (e.g., noisiness, explainability). Given that more and more people are using smartphones and wearables to track their health data, quantifying themselves like never before, Mark’s expertise is tremendously important. We hope you enjoy the episode.

What do we discuss?

Minute (2:24) Behind the Scenes of Machine Learning (ML) & Artificial Intelligence (AI) in the health setting

Minute (13:26) The hype around AI & ML

Minute (22:44) Applicability, gadgets and the quantified self in daily life

Minute (31:11) Accurate, reliable, and correct data

Interested in more?

• ‘Helping AI explain itself’ paper by Bülent Ündes on the explainability of complex models: https://stress-in-action.nl/helping-ai-explain-itself-faster-and-smarter-with-startgrad/
• Machine Learning for the quantified self by Mark Hoogendoorn: https://www.bol.com/nl/nl/f/machine-learning-for-the-quantified-self/9200000084217477/
• Mark's research at VU Amsterdam: https://research.vu.nl/en/persons/mark-hoogendoorn/

Credits:

Host: Marcos Ross

Supported by: Artemis Stefani, Myrte Schoenmakers, Noa van Zwieten, Merel van den Berg

Production: Anouk Weverling

Music: Marcos Ross