You are invited to AIMed’s Fall Breakfast briefing in Boston or Join us at the AIMed’s first fall October US roadshow in Boston, featuring SOPHIA GENETICS.
Boston is a hub for medical,research and technological innovations. The ecosystem is flourishing and growing. AIMed is very delighted to be hosting its first fall breakfast briefing on October 11th in Boston, a city that is been known as mecca of medicine.
The AI in Medicine (AIMed) meeting was inaugurated in October of 2013 as a satellite meeting to the Pediatrics2040 meeting with about 300 attendees. The first full AI Med meeting was in December of 2016 followed by another meeting in 2017 with close to 600 attendees (with almost half being clinicians) and over 60 faculty members from over 20 countries. In addition, over 150 abstracts were presented during this meeting.
In 2018, AIMed successfully hosted its inaugural Europe meeting in London, which is followed by it’s Asia inaugural meeting to be hosted in Hangzhou, China in November (8-10th), and back in the United States for AIMed North America at Dana Point, California in December (12-15th). AIMed Portfolio provides year-round education and networking opportunities for clinicians, solution-providers and thought-leaders alike through a series of international meetings, breakfast briefings, magazine and online-content.
Our Boston breakfast briefing with SOPHIA GENETICS will be a panel format. The details are below.
Panel Title: A Clinician’s guide to AI in Clinical Genomics.
Topics to be discussed will include:
- The global adoption of cutting-edge Data-Driven Medicine applications
- Interconnectivity and knowledge sharing between medical centers, medical professionals, pharma and advocacy groups is the key to precision medicine
- The use of AI to analyze the millions of data points available in the medical field will give us the ability to unlock the potential of Data-Driven Medicine.
By hosting this breakfast we want to bring audiences together to learn and share where AI could match with clinical genomics were they have large amount of data to work with.