Symposium on Artificial Intelligence for Learning Health Systems

May 22–24, 2022

Bermuda

Symposium Updates

UPDATE 2/04/22: Registration for SAIL 2022 on May 22–24 is now open!

  • Limited in-person attendance is available. If interested, please contact us.
  • Register to view the livestream of the following sessions (Eastern times shown):
    • Opening Keynote: Leveraging Autonomous AI to Solve Disparities in Healthcare (Michael Abramoff | May 23 12:00–12:45pm ET)
    • Fireside Chat: AI in Medical Publishing (editors of NEJM, Nature Medicine and The Lancet Digital Health | May 23 2:30–3:30pm ET)
    • Closing Keynote: Regulating AI in Healthcare: Now and into the Future (Amy Abernethy | May 24 10:00–10:45am ET)

UPDATE 10/07/21: We are pleased to announce the new dates for SAIL will be in May 2022. Registration will open soon.

UPDATE 9/24/21: Because the Center for Disease Control recently changed the travel advisory for Bermuda to a “Level 4: DO NOT TRAVEL”, it is with much regret that we announce that the SAIL symposium is being postponed until Spring 2022. The new dates will be announced as soon as we can coordinate with the venue and our speakers.

UPDATE 5/4/21: Announcing the 2021 Call for Abstracts, due June 25, 2021! Please note that in-person capacity for the event is limited, but spaces will be reserved for those with accepted work.

UPDATE 3/11/21: Read More Intelligent Medicine: Leaders in biomedical informatics chart roadmap for harnessing the promise of medical AI, a summary of our 2020 Virtual Pre-Symposium.

About

The Symposium on Artificial Intelligence for Learning Health Systems (SAIL) is a new annual international conference exploring the integration of artificial intelligence (AI) techniques into clinical medicine. SAIL, which will be held in Hamilton, Bermuda, provides a forum for clinicians, clinical informaticians and AI researchers to discuss approaches and challenges to using these approaches in the healthcare domain.

Researchers from the fields of clinical informatics and machine learning have been concerned for decades with bridging the gap between quantitative research and clinical practice. Less appreciated, perhaps, is a potential disconnect between the communities of clinical informatics and machine learning researchers themselves. Clinical informaticians, for their part, have unique experience integrating their tools within hospital IT systems, and working with administrators, privacy policies, and quality improvement officers. Computer scientists, for their part, have frequently spearheaded the development of novel methodological tools and high-performance computing. Both of these research communities seek to work effectively with clinicians, who provide important domain expertise and interaction with patients. One explicit goal of this conference is to foster the translation of AI to the clinic by catalyzing collaborations that leverage the strengths of all three of these communities. A second goal is to facilitate discussion among diverse stakeholders- the three communities described above as well as funders and decision makers around the non-technical barriers to better care through AI. The inaugural Program Committee of SAIL is led by Jessie Tenenbaum (Duke), Jason Moore (UPenn), Nicholas Tatonetti (Columbia), Suchi Saria (Johns Hopkins), and Isaac Kohane (Harvard).

SAIL is supported by