AiTraC: The Inaugural AI in Transplantation Meeting

2020

POSTPONED
1 Tower Place
South San Francisco, CA
CareDx

AiTraC: The Inaugural AI in Transplantation Meeting

2020

POSTPONED
1 Tower Place
South San Francisco, CA
CareDx

Conference Information

Transplantation is saving more lives every day, yet we still have not substantially improved long-term outcomes.

There is a need for better organ utilization, allocation, matching, and post-transplant follow-up.  Applying AI and machine learning can enable utilization of the large amounts of data we capture for each transplant patient and pave the way for personalized medicine.

At AiTraC 2020: The Inaugural AI in Transplantation Meeting, we aim to provide an introduction to AI in the field of transplantation and build a consensus of where and how this technology can best be applied. This two-day conference is designed to help attendees understand the current thinking and future direction of these advanced applications and how best they can be utilized to improve long-term patient outcomes.

Learning Objectives

  • Develop an understanding of how AI can help build personalized care for better transplant patient management
  • Hear feedback from transplant patients on the impact of AI for comprehensive care
  • Learn how AI can impact organ allocation and potential to improve utilization of organs
  • Discuss the potential for AI to improve center management

Audience

Bringing together healthcare professionals and AI technology experts to discuss how data solutions can improve transplant patient outcomes.

Why You Should Attend

While many of the sessions are geared to transplant, the forum is also an excellent opportunity for clinical physicians and surgeons, and those working in the cutting edge of artificial intelligence to enhance their understanding. Sessions help to increase collaboration between the transplant community to the technology industry, and to expand transplant professionals’ knowledge of how we all can utilize artificial intelligence to improving patient outcomes.

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