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ETC on Artificial Intelligence (AI)

The SAR Emerging Technology Commission (ETC) on Artificial Intelligence (AI)

The goal of an ETC is to develop, validate and educate on emerging imaging technologies that will impact abdominal radiology and patient care.   This is similar to a Disease-Focused Panel (DFP) but with an emphasis on the technology, rather than the disease.




To develop, validate and educate on artificial intelligence and machine learning technology that will impact the clinical practice of Abdominal Radiologists and the quality of patient care they deliver. 




·       To develop and validate machine learning algorithms that solve a problem related to Abdominal Radiology.


·       To develop educational materials that advance knowledge of AI methodologies, algorithms, and research approach in order to accelerate the appropriate use of AI in Abdominal Radiology.


·       To develop infrastructure and relationships with industry collaborators, SAR DFPs, data scientists, biostatisticians, informaticists, and other medical societies, that enhance the mission of the SAR ETC on AI.


·       To develop mechanisms that enhance collaboration (e.g. requests for applications, or RFAs) with SAR DFPs who have a defined plan for development or validation of a highly impactful machine learning algorithm.

How to Get Involved

The SAR AI Challenge closed on October 15, and proposals are no longer being accepted for this  competition.  We welcome your participation in the important work conducted by the SAR AI ETC (Emerging Technology Commission).  For more information on making a contribution, go to 

Your submissions are solicited for the SAR AI Challenge 2020, sponsored by the Artificial Intelligence ETC and Disease Focus Panels. 


For this competition and collaboration with the ACR Data Science institute,

we are seeking use cases or clinical scenarios in abdominal radiology where artificial intelligence tools should be developed.

Nobody knows the answer to this better than you.


The three teams submitting the top three proposals will be invited to pitch their ideas at “SAR Tank” at the upcoming 2020 SAR Annual Meeting in Maui, Hawaii


The submission form provided guidance on the qualities for selecting a good SAR AI clinical scenario and a sample submission.


Submission deadline is October 15, 2019

·      One member of the team must be a SAR member

·      If your team is one of three selected to make a presentation, one member of the team must be willing to travel to the 2020 SAR Annual Meeting March 1-6, 2020.  You will receive notification on December 15, 2019.



More Information about the SAR Emerging Technology Commission (ETC) on AI




SAR Members 

Andrew Smith MD PhD FSAR

Position: Associate Professor and VC of Clinical Research

Institution: University of Alabama at Birmingham

AI Interests: tumor response, CT image reconstruction, patient triage


George Shih MD

Position: Associate Professor and VC of Informatics

Institution: Weill Cornell Medical College


Mark Kohli MD

Position: Associate Professor and Director of Informatics

Institution: UCSF


Ronald Summers MD PhD

Position: Senior Investigator, Chief Clinical Image Processing Services

Institution: NIH


Mark Kovacs MD

Position: Assistant Professor, IT Medical Director, Radiology

Institution: MUSC


Paul Murphy MD PhD

Position: Assistant Professor

Institution: UCSD


Andrea Rockall

Position: Professor, Clinical Chair of Radiology

Institution: Imperial College of London


Iva Petkovska MD

Position: Staff Radiologist

Institution: MSKCC


Hanna Zafar MD MHS

Position: Associate Professor, Co-Dir Automated Radiology Recommendation Tracking Engine

Institution: UPHS


Daniel Rubin MD MS

Position: Professor, Dir Biomedical Data Science

Institution: Stanford


Paul Chang MD

Position: Professor, VC Radiology Informatics

Institution: University of Chicago School of Medicine


Stacy O’Connor MD MPH

Position: Assistant Professor

Institution: Medical College of Wisconsin


Sarah Bastawrous DO

Position: Assistant Professor

Institution: University of Washington


John Mongan MD

Position: Assistant Professor

Institution: UCSF 


Short-term Goals and Projects to Support the Goals (within next 2 years):


·       Establish ETC membership to includes SAR members, non-radiology consultants, and industry partners with expertise in AI.

·       Establish relationships with other Societies that have an interest in AI and Abdominal Radiology.

·       Develop an AI educational track at the SAR Annual Meeting that includes Workshop Sessions, Plenary Lectures, and a Hands-on Workshop.

·       Propose at least 2 multi-institutional research projects that are impactful, achievable, and focus on AI in Abdominal Radiology.

·       Establish a mechanism to receive and review research proposals from DFPs that are impactful, achievable, and focus on AI in Abdominal Radiology.




In addition to traditional emails, the SAR ETC on AI will utilize Google documents and group messaging applications such as Slack, whereby the SAR ETC on AI may connect with existing Slack groups such as the Society of Imaging Informatics in Medicine (SIIM).


For information about this effort, contact


 How to Get Involved:

Participate in the AI session at the SAR annual meeting in Maui, Hawaii March 1-6, 2020

We also created a Slack for SAR members to join and participate. Please join using this link:


Email: | Phone: 847-752-5355
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