AI for Health Symposium
On November 2, Radboudumc AI for health organized a symposium on the impact of AI on healthcare.
On November 2, Radboudumc AI for health organized a symposium on the impact of AI on healthcare.
Applications for The fourth edition of the AI for Health course, starting on the 3rd of February 2023 are now open. Please apply before the 1st of December to join the course. The AI for Health program aims to advance AI innovations in healthcare, by providing an AI course for …
Friday, July 1st, 2022, the third AI for Health course concluded with a meeting where all participants presented their AI projects' results. About 25 Radboudumc employees and external health professionals from different backgrounds followed the third course. The fourth edition is scheduled to commence in February 2023, if you are …
Applications for The third edition of the AI for Health course, starting on the 11th of February 2022 are now open. Please apply before the 17th of December to join the course. The AI for Health program aims to advance AI innovations in healthcare, by providing an AI course for …
Friday, February 12, 2021, the second edition of the AI for Health course concluded with a meeting where all participants presented the results of their AI projects. The course was followed by about 25 RadboudUMC employees from various backgrounds, offering them a deep dive into the possibilities of AI in …
You are invited to submit new MSc project proposals aimed at solving clinical problems with AI.
Friday, September 25, 2020, the first AI for Health course concluded with a meeting where all participants presented their AI projects' results. About 25 Radboudumc employees from different backgrounds followed the first course. The second edition has now started and will run until February 2021. The third edition is scheduled …
The second edition of the AI for Health course will be starting on September 18th 2020.
Save the date and call for abstracts for the first Virtual Conference on Computational Audiology (VCCA) to be held on June 19, 2020.
The Gleason score suffers from significant inter-observer variability. This problem could be solved by the fully automated deep learning system developed by Wouter Bulten and his colleagues. Their work appeared in The Lancet Oncology last month.