DASH Webinar: Trustworthiness of AI applications in Healthcare

Area: Health Data

News

Area: Health Data

Published March 22, 2021

Artificial intelligence (AI) applications have the potential to transform and accelerate healthcare. However, when starting implementing AI in healthcare, you want to make sure that systems are reliable and adhere to the best practices in responsible machine learning development.

During our DASH webinar (Tuesday April 20th, 2021), Oge Marques, an award-winning educator and researcher, and Christian Garbin, a researcher in the field of implications of machine learning products, will discuss how to reduce or even prevent errors in AI healthcare applications, increase transparency of the AI lifecycle and improved reporting through the use of recently introduced AI checklists and guidelines. As examples, they will demonstrate the use of Datasheets for datasets for the ChestX-ray8 dataset and Model cards for the CheXNet model.

Our speakers:

Oge Marques (PhD) is professor of Engineering and Computer Science at Florida Atlantic University (FAU). He is also advisor for the "AI in Healthcare Interest Group" in the College of Medicine and author of 11 books and more than 120 scholarly publications in the area of Visual Artificial Intelligence. Oge is a Sigma Xi distinguished speaker and a senior member of both the IEEE (Institute of Electrical and Electronics Engineers) and the ACM (Association for Computing Machinery). He is actively working on the intersection between AI and Radiology with medical professionals, researchers, and students from FAU, NIH, Stanford, and other universities and research labs in the US and abroad. In addition, he is an associate member of the Radiological Society of North America (RSNA), a corresponding member of the European Society of Radiology (ESR), and a member of the Society for Imaging Informatics in Medicine (SIIM) and the European Society of Medical Imaging Informatics (EuSoMII). He is currently working on the book "AI for Radiology", scheduled to appear later this year.

Christian Garbin, researcher in the field of implications of machine learning products, has held several positions in software development, management, and staff in the telecommunications and financial industries, working with highly-available, high-performance products. Christian received his M.S. degree in Computer Science from FAU in 2020. His M.S. thesis focused on how to improve reporting, increase transparency, and reduce failures in machine learning applications in healthcare. Currently, he is a senior architect and a distinguished expert at Atos, and PhD candidate at FAU, focusing on the implications of machine learning products for users and society at large.