Building safe and reliable AI for health care – insights from real world use cases
Together with our experts, we share some insights on how you can build safe and reliable AI for health care and share some interesting real-world use cases. Join this one-hour webcast on 2nd June 2021 at 08 am CEST.Register here
Methods from the fields of Machine Learning and Artificial Intelligence have been applied successfully in many different domains and industries. The health sector is no exception to this. The digitization of medical records and diagnostic data along with the increasing amount of patient-generated data have placed high hopes when it comes to healthcare Machine Learning solutions.
As of today, no clear norms or guidelines for the development of AI devices in the medical field exist. Due to this and despite regular publications of papers on medical AI, only few approved applications of AI can be found in the medical practice. This is unfortunate, as AI has a great potential to curb rising healthcare costs and improve the patient experience.
In this webcast we will show how AI projects can be executed in a regulated setting. We will cover in depth the real world medical machine learning projects of building a decision support system for diagnostics, getting FDA-clearance for a wearable medical device and developing a machine learning pipeline for a surgical robot. These examples will help to show how the Data Science Process can be adapted at each phase to satisfy regulatory requirements.
Date: 2. June 2021
Start: 08.00 am CEST
Duration: 1 hour
Bardia M. Zanganeh
Senior Business Development Manager
Bardia M. Zanganeh is responsible for the Life Sciences and Healthcare practice in Switzerland. He serves leading healthcare institutions on all technology agenda issues. His primary areas of focus include digital innovation, business model transformation and product innovation. He also serves providers as well as medical technology and pharmaceutical companies. He has a background in engineering, consulting and entrepreneurship and is a lecturer at the University of Applied Sciences in Business Administration in Zurich.
Principal Data Scientist
Gabriel Krummenacher leads the Data Science Team at Zühlke and has several years of experience in conducting data analytics and machine learning projects. His main focus is on medical machine learning applications and bringing prototypes to production. He holds a PhD and M.Sc. from the Institute for Machine Learning at ETH, where he worked on scalable methods for large-scale and robust learning, wheel defect detection and sleep stage prediction with deep learning.