Frontier of AI-Assisted Care Scientific Symposium (FAC)

Sept. 18-19, 2019   ||   Stanford University, CA

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Symposium Overview

The Frontier of AI-Assisted Care Scientific Symposium (FAC) 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 Dr. Fei Fei Li and Dr. Arnold Milstein, with the generous support of the Gordon and Betty Moore Foundation.

FAC’s purpose is to promote collaboration amongst researchers who share the vision of a computer-assisted, rapid learning healthcare system, capable of bridging what the National Academies describe as a “chasm” between potential and actual efficiency and quality of care.

The symposium content will be based upon the selected abstracts. There will be six topic areas (see below), from each of which we will invite the winner to give a 10-minute presentation. The 2nd and 3rd place winners will be invited to the stage for a moderated discussion. All submissions will have the opportunity to present a poster. All abstracts will be reviewed by the editorial team of Nature Medicine, along with a carefully selected scientific committee.

Call for Scientific Abstracts : Topics

We invite abstracts on six uses of AI to improve the value of healthcare and self-care for patients.
Topic 1 : AI to improve diagnostic accuracy
  • E.g., machine-assisted diagnostic interpretation of medical images, pathology slides, or other health data.
Topic 2 : AI to improve selection of treatment options
  • E.g., use of clinical and other data sources to fill gaps when no high-grade trial evidence exists to guide treatment decisions (estimated to be in 85% of these circumstances).
Topic 3 : AI to establish/improve step-by-step clinical pathways for applying treatment options
  • E.g., there is growing evidence that use of software-based clinical pathways improves healthcare value for cancer patients.
Topic 4 : AI to detect and rapidly correct failures in intended clinician, patient and in-home 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 by health professionals. Illustrative failures include lapses in clinician hand hygiene, and patients’ adherence to prescribed medications.
Topic 5 : Innovation in AI methods that improve Al’s impact on healthcare
  • E.g., use of novel statistical methods and/or data sets to infer changes in patients’ health goals.
Topic 6 : AI enabling patients to self-assess symptoms, select and implement self-care options to avoid 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.

Abstract Submission & Review

The review process will be coordinated by the editorial team of Nature Medicine. Reviewers will rate abstracts based on scientific merit and potential for impact on healthcare value at scale within 10 years, especially for medically fragile and costly population segments. Examples of fragile and costly patients are those receiving inpatient care, frail seniors seeking to maintain independence and dignity at home, or children whose lifetime health is affected by early life experiences.

Submitted abstracts should describe the topical background, methods, results and implications for improving the value of care, and indicate the category in which these should be considered among those described above. Abstracts can be considered for oral presentations in a maximum of 2 categories. All authors whose abstracts exceed a threshold score (to be determined after review) may opt to have their abstract published, via an online appendix, to a summary report on conference proceedings.

Submission Details

  • The maximum abstract length is two pages (excluding references).
  • All submissions should be in 11-point Times New Roman font with 1” margins on all sides.
  • Because reviewers will be blinded to the author’s identities, do not include the names of authors, institutions, or any other identifying information in the initial submission.
  • Research that has been previously published elsewhere, or is currently in submission, may be submitted.
  • To submit, please email to with the subject line “FAC Abstract Submission.”
  • Deadline: March 1, 2019


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