Don't Thwart Innovation!

Philipp Grätzel von Grätz
Published on January 2, 2019
Investment and data availability are necessary elements of an AI framework in healthcare.

Data must be more readily available

“We need measures to make capital and data more readily available,” said Professor Björn Eskofier, head of the Machine Learning and Data Analytics Lab at Friedrich-Alexander University Erlangen-Nuremberg.

The political framework in healthcare needs to catch up with the possibilities of AI.

“We have a lot of catching up to do”

“We have a lot of catching up to do," said Tino Sorge, a member of the Health Committee of the German Bundestag.

Test tracks could speed up the implementation of an AI framework for healthcare.

Making the case for personal health managers

Professor Erich Reinhardt, President of the Board of Medical Valley European Metropolitan Region Nuremberg, suggested “test tracks” for digital health – similar to test tracks for autonomous driving.

Treating physicians want to understand how AI tools arrive at their recommendations.

Black box algorithms: Can an AI application be liable?

Thomas Friese, PhD, General Manager Data Architecture & Technology Platforms at Siemens Healthineers, described the concept of “explainable AI.”

Legal obstacles hinder the widespread implementation of AI in healthcare.

Professor Christian Dierks of Dierks+Company suggested to give AI its own legal status to make it both liable and insurable.

By Philipp Grätzel von Grätz

Philipp Grätzel von Grätz lebt und arbeitet als freiberuflicher Medizinjournalist in Berlin. Seine Spezialgebiete sind Digitalisierung, Technik und Herz-Kreislauf-Therapie.