InterContinental San Francisco
888 Howard Street
San Francisco, CA 94103
This is a 2.5 day program focused on artificial intelligence (AI) in Cardiology targeting clinicians, clinical investigators, computer scientists, data scientists and engineers interested in current and potential applications of AI in cardiovascular medicine.
The program has a hybrid format including keynote speakers, regular lectures, and brief presentations of ongoing or successful AI projects in Cardiology, as well as breakout sessions and poster sessions. The didactic and keynote presentations will cover topics of interest related to AI in Cardiology including a brief history of AI, early applications of AI in general and specific to healthcare, and a review of pioneering applications of AI in Cardiology. We will also provide a general overview of the general steps to carryout research involving AI, a summary of key concepts to better understand the principles of AI in medicine, and to clarify concepts that are commonly misunderstood related to AI.
In this program we will also provide some attendees a forum to present their current projects with a special format with limited slides and time for questions. Some lectures, particularly the breakout sessions, will be focused on specific areas of AI in medicine such as prediction tools, screening tools, natural language processing in healthcare and medical informatics, imaging processing, and clinical decision making. There will also be talks providing attendees general tools on how to publish manuscripts on AI and critical steps to pursue intellectual property rights.
To reinforce the knowledge gained from the course and ensure lasting value to each attendee’s practice, Mayo Clinic provides access to all course presentations with voice-over recordings after the completion of the course.
Upon conclusion of this program, participants should be able to:
- Summarize a general overview of the current state of AI in Cardiology
- Synthesize basic principles of machine learning including a summary of the most common methods for supervised and unsupervised machine learning
- Assess potential applications of AI in Cardiology applied to prognostic tools, clinical decision making, imaging processing, and informatics
- Analyze the current challenges conducting projects developing AI and discussing potential ways to overcome those challenges
- Summarize and critically analyze ongoing or completed projects on AI in Cardiology
The course is intended for cardiologists, critical care physicians, clinical investigators, computer scientists, data scientists, engineers, and other medical and computer professionals who are interested in a contemporary review of machine learning and its applications to medicine and specifically to cardiology.