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.
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, payors, and regulators).
We invite submissions for abstracts, which will be selected for podium and poster presentations. Abstracts should contain either: 1) new and unpublished work, 2) highlights of recently published work or 3) overarching research theses. Abstracts should be well-supported with references to prior work.
Research themes include: integrating AI into clinical workflows, deploying machine learning systems at scale, and methods for evaluation and monitoring of clinical ML systems. Topics of particular interest include fairness, privacy, generalizability across institutions over time, real-time prediction, and regulatory compliance. Descriptions of novel methods for real-world evidence, causal inference, and precision medicine are also welcome. We highly encourage work that involves interdisciplinary collaboration across AI researchers, clinicians, and informaticians.
Abstracts have a 500 word limit, excluding references, figures, and figure captions. Abstracts should be submitted in PDF format. Abstract submissions are unblinded, and may contain content that has been published within the last 12 months. Abstracts will be selected for poster and oral presentations by the Program Committee.
How to Submit:
Please fill out the form here to submit your abstract.
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