Symposium on Artificial Intelligence for Learning Health Systems

October 19 - 21, 2020


Coronavirus Update

UPDATE 3/12/20: Thank you for your patience as we have been working to quickly respond to the impact of COVID-19. SAIL 2020 is now rescheduled for October 19-21, 2020 at the same venue, the Fairmont Hamilton Princess in Bermuda. We hope you will be able to join us for the new dates. We are currently working with our invited speakers to retain as much of our program as possible, and we will post program updates ASAP. Our old hotel room block at the Fairmont will now be canceled, so if you will be attending SAIL in October you will need to make a new hotel reservation. We will send out the new booking link as soon as it is available. If you will be attending SAIL in October, and you have already registered, you do NOT need to register again. If you are unable to join us in October, then you will need to contact to CANCEL your registration and request a refund.

UPDATE 3/6/20: We are sad to announce our decision to postpone SAIL2020. We could not in good conscience ask you and others to take the risk of exposure to COVID-19, or potential quarantine. Many of our speakers are also now unable to participate. We will post our new date as soon as it is confirmed. For those of you who have already booked travel and decide to cancel your trip to Bermuda, please contact your airline or travel agency so that they can hold the tickets for later travel. Please direct questions to


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 on April 27-29, 2020, 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