The Symposium on Artificial Intelligence for Learning Health Systems (SAIL) is an annual international conference launched in 2020 to explore the integration of artificial intelligence (AI) techniques into clinical medicine. SAIL will be held in Puerto Rico on May 7–10, 2024.
An explicit goal of this conference is to foster collaboration between methodologists and key healthcare decision makers such as clinicians, hospital administrators, regulators and payors. SAIL will feature leading voices from each of these backgrounds to shed light on opportunities for and obstacles to the deployment of clinical AI systems.
A second key goal of SAIL is to better integrate the complementary experience of the clinical informatics and machine learning communities. Clinical informaticians, for their part, have an extensive history of 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. SAIL is designed to bring together the best of each of these closely related but often disconnected communities.
SAIL will feature invited presentations to expose AI practitioners to the clinical workflow and administrative challenges that commonly prevent real-world adoption. Panels will convene seasoned leaders who have overseen the implementation, adoption, and regulation of real clinical AI systems in practice. Tutorials will provide hands-on exposure to open-source tools for integrating apps with hospital IT systems. Finally, we solicit abstracts for podium or poster presentations designed to generate fruitful discussion (and debate!) among conference attendees from diverse backgrounds (clinicians, clinical informaticians, computer scientists, payers, and regulators).
The inaugural Program Committee of SAIL was led by Jessie Tenenbaum (Duke), Jason Moore (UPenn), Nicholas Tatonetti (Columbia), Suchi Saria (Johns Hopkins), and Isaac Kohane (Harvard).