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Top 5 Healthcare and Medical Research GTC Sessions

NVIDIA GTC is the premier AI and deep learning conference, providing training, insights, and direct access to experts on advancing life science, pharmaceutical, and biomedical research.

Explore how GPU-driven AI is enabling personalized medicine, improving population health management, and enhancing patient outcomes. Dive deep into the latest AI advancements in genomics, medical imaging, and drug discovery.

Here is a breakdown of the sessions listed in the video.

#5: AI and Machine Learning in Radiology: A Reality Check

Paul Chang, Professor & Vice Chairman, Department of Radiology, University of Chicago School of Medicine

Get a realistic perspective on how machine learning and artificial intelligence can add value to radiology. We’ll review the significant challenges involved in implementing and integrating machine learning/artificial intelligence into radiology’s existing workflow and IT infrastructure. We’ll also discuss strategies for preparing the radiology department and IT for machine learning/artificial intelligence.

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#4: AI in Diagnostic Imaging: An Opportunity to Reinvent the Clinical Workflow

Tessa Cook, Assistant Professor of Radiology, Penn Medicine

We’ll talk about how AI can reinvent the workflow for radiology. Understanding the current workflow and challenges in radiology is important when developing potential AI solutions, but developers must avoid simply replacing existing workflow steps with AI. We’ll discuss how using AI to refashion the workflow could help radiologists and physicians in other diagnostic imaging specialties deliver more effective, personalized, cost-effective, and accessible care to patients. We’ll describe challenges specific to patient care in radiology, brainstorm solutions, and describe AI initiatives we’re piloting.

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#3: Developing a Roadmap for Machine Learning in Clinical Radiology

Christopher Hess, Professor and Chair, Radiology & Biomedical Imaging, University of California, San Francisco

We will outline strategies designed to incorporate emerging artificial intelligence and machine learning into the clinical practice of diagnostic radiology, the primary entry point for imaging in the U.S. healthcare system. We’ll discuss the underlying radiology value chain to explain the architecture of existing radiology information management systems for imaging. We will highlight the centrality of imaging in guiding patient care to outline the opportunities and barriers to adopting AI and ML in clinical practice, and we’ll explore how AI and ML are poised to transform imaging delivery for certain medical domains to the benefit of patients.

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#2: Machine Learning in Action within a Large Regional Healthcare System (Geisinger)

Brandon Fornwalt, Associate Professor, Geisinger

Learn how Geisinger, one of the first healthcare systems to install a DGX-1 inside a clinical network, is developing and implementing machine learning solutions to improve patient care. We will show how we leveraged the DGX-1 to analyze large clinical datasets to tackle clinically relevant problems. Specific applications include: (1) using 46,583 clinically acquired 3D computed tomography images of the brain to develop and implement a deep learning model to efficiently prioritize radiology worklists for quicker diagnosis of intracranial hemorrhage, (2) using deep learning to analyze more than 200,000 echocardiographic videos of the heart to accurately predict patient survival, (3) analyzing more than 1 million 12-lead electrocardiographic tracings from the heart to predict future clinically relevant events, and (4) using machine learning to optimize evidence-based care delivery for a population of more than 10,000 patients with heart failure.

#1: High-Performance Medicine to Go Deep

Eric Topol, MD, Founder and Director, Scripps Research Translational Institute

We will talk about how artificial intelligence and deep learning are beginning to affect medicine at three levels. For clinicians, AI enables rapid, accurate image interpretation. For health systems, it improves workflow and has the potential to reduce medical errors. For patients, it makes it possible to process their own data to promote health.

To see all healthcare sessions, see the GTC catalog.

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