Frontier of AI-Assisted Care (FAC)
Stanford, CA || September 18-19, 2019
Thank you for attending FAC 2019
We want to celebrate the connections made at this event. Please share with us any collaborations or partnerships that sprouted from your time at the symposium
Send all information to Sara Kelly at firstname.lastname@example.org.
The Frontier of AI-Assisted Care (FAC) Scientific Symposium aims to accelerate progress in methods and applications of artificial intelligence (AI) that enable excellent healthcare at a lower cost, by convening the most accomplished university and industry research teams. The symposium is co-hosted by Stanford faculty members Fei Fei Li and Arnold Milstein, with the generous support of the Gordon and Betty Moore Foundation, the National Science Foundation, Nature Medicine and Stanford Human-Centered AI.
Our purpose is to promote collaboration among researchers sharing the vision of a computer-assisted, rapid learning healthcare system that eliminates what the National Academies describe as “a chasm” between potential and actual efficiency and quality of care.
The symposium content will be based upon submitted abstracts. Abstracts are assessed by the editorial team of Nature Medicine and its scientific committee. In six topic areas (see below) we invite one author from the top two-rated abstracts to give a 10-minute presentation. Two authors in each topic will be invited to the stage for a moderated discussion. Selected submissions have the opportunity to present a poster.
Topic 1: AI to improve accuracy of diagnosis and health risk assessment
E.g. machine-assisted interpretation of medical images, pathology slides, or other health data to arrive at a diagnosis or asertain health risk.
Topic 2: AI to improve selection of treatment options
E.g. machine-assisted treatment decisions where no high-grade trial evidence exists.
Topic 3: AI to improve step-by-step clinical pathways used to apply treatments
E.g. software-based clinical pathways for cancer care.
Topic 4: AI to detect and correct failures in clinician, patient and lay care-giver treatment actions inside and outside of healthcare facilities
E.g. use of sensors in inpatient and home settings to detect failures in intended physical actions such as administration of prescribed medications.
Topic 5: Innovation in AI methods that increase AI’s capacity to improve healthcare
E.g. use of novel statistical methods and/or data sets to infer changes in patients’ health goals.
Topic 6: AI to improve patients’ ability to self-assess symptoms, select and implement self-care options to avoid use of or more successfully partner with health care professionals
E.g. uses of AI that enable patients to be more effective in diagnosis, and/or treatment option selection.
Call for Abstracts: Now Closed
Thank you for your interest. The call for abstracts is now closed. The review process is underway by the editorial team of Nature Medicine. Please join our mailing list to be notified of future opportunities.
This is event is hosted by Professors Fei-Fei Li and Arnold Milstein of Stanford