PRESENTATION TITLE
How can AI Improve the Conduct of Clinical Trials and Care of Patients in the Cancer Centers

PRESENTER(S)
Bradley J. Erickson, M.D., Ph.D.
Professor of Radiology, Consultant, Department of Radiology | Consultant, Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota

PRESENTER BIO

The research interests of Bradley J. Erickson, M.D., Ph.D., include computer-aided diagnosis and the use of computer technologies to extract information from medical images for diagnostic, prognostic and therapeutic purposes. This research includes the development and validation of algorithms that can detect progression, regression or risk of disease, and the prediction of molecular markers from medical images.

Dr. Erickson is also actively developing a system to promote team science, initially pursuing on imaging-focused research, but with connections to genomics, pathology and clinical data.

Focus areas

• Quantitative imaging. Work in this area involves applying methods to better characterize and treat disease using quantitative metrics such as volume or texture.

• Computer-aided diagnosis. Dr. Erickson is using methods of machine learning and deep learning to identify the most important information in images and reliably apply that information to the care of patients.

• Polycystic kidney disease (PKD). Imaging is used to measure and characterize the nature and progression of PKD.

VIEWING LOCATIONS
Zoom Registration Link

LEARNING OBJECTIVES

• Demonstrate the current technology and capabilities of Deep Learning applied to medical images.
• Identify the strengths and weaknesses of Deep Learning methods as they are applied to cancer imaging.
• Assess the potential for AI to improve the quality and efficiency of clinical trials.

ATTENDANCE / CREDIT
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TRANSCRIPT
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