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Release Date
March 6, 2018

Expiration Date
March 31, 2020
 

Faculty


Paul J. Chang, MD, FSIIM
Professor & Vice-Chairman, Radiology Informatics

University of Chicago School of Medicine
Medical Director, Enterprise Imaging
Medical Director, SOA Infrastructure
University of Chicago Hospitals

 

 

Equipment Requirements
PC: Win7/Win8, Pentium processor or faster, at least 2GB RAM, Internet Explorer version 10/11, Mozilla FireFox version 33.0.

MAC: OS version 10.8/10.9, Intel processor, at least 2GB RAM, Safari version 6.2, FireFox 33.0

 

A certified one-hour lecture for Radiology Administrators and Managers, Radiologic Technologists, Lead Technologists, and Modality Leaders

 
Format: Online Course
Physician Credit: no MD credit available
RT Credit: 1.0 ARRT Category A
Tuition: Free

 

OVERVIEW AND EDUCATIONAL OBJECTIVES
Artificial intelligence (AI) and deep learning continue to be hot topics in healthcare and have special applicability in Radiology. AI is not new to Radiology. Computer aided detection (CAD) technology has been relied upon for several years, especially in breast and lung imaging studies, by using pattern recognition software to identify suspicious features.1

This lecture will discuss both machine learning and deep learning and how they differ from traditional programming models, current IT infrastructure challenges that will need addressing in preparation for seamless and effective integration of a service-oriented architecture, and what Dr. Chang believes will be the true benefits of deep learning applications for Radiology: optimizing workflow to eliminate “busy work” that adds to variability, burnout, and inefficiency.

1. Castellino RA. Computer aided detection (CAD): an overview. Cancer Imaging. 2005;5(1):17-19.

Educational Objectives
After completing this activity, the participant should be better able to:

  • Describe the relationships among computer science, data science, artificial intelligence, machine learning, and deep learning
  • Explain the primary difference between a traditional programming model like PACS and machine learning
  • Discuss the benefits of employing deep learning applications in Radiology

 

CRA-Logo-High-ResRadiologic Technologist and
Administrator Continuing Education

This program has been approved by the Association for Medical Imaging Management (AHRA) for 1.00 hours ARRT Category A continuing education credit. This program has been approved for CRA renewal credit under the Operations Management (OM) and Communications and Information (CI) domains.  Certificates of Credit accepted by the American Registry of Radiologic Technologists (ARRT).

HOW TO RECEIVE CREDIT
There are no fees or prerequisites to participate in this program. In order to view this lecture, you must have an account and register for this lecture at www.icpme.us.

  • To view the lecture, log-in to your account at www.icpme.us. Click on the lecture title and follow the link to the recording.
  • At the conclusion of the lecture, close the Vimeo recording window.
  • Return to the course in your account at www.icpme.us.
  • From the COURSE HOME page, click the button for POSTTEST and for EVALUATION.
  • A passing grade of at least 75% is required to receive credit. You may take the test up to three times.
  • Upon receipt of a passing grade, you will be able to print a certificate of credit from your account at www.icpme.us
  • Your certificate of credit will remain in your account at www.icpme.us as a permanent record of your participation.

FACULTY
Paul J. Chang, MD, FSIIM
After graduation from Stanford University with his medical degree, Dr. Chang completed a residency in Radiology at Stanford. He is active in numerous research and development projects related to imaging informatics as well as enterprise-wide informatics interoperability and workflow. Dr. Chang is an internationally recognized expert in the field of imaging informatics. His work in workstation design has resulted in presentation and navigation models that have been adopted by most PACS (Picture Archiving and Communication System) vendors. A novel lossless wavelet-based image distribution mechanism, dynamic transfer syntax (DTS), was co-invented by Dr. Chang; this technology was subsequently commercialized by the creation of Stentor PACS, which was acquired by Philips Medical Systems. This PACS system is used by several hundred hospitals worldwide and is a world-wide leader in market share. Dr. Chang has served as a member of the Radiology Society of North America (RSNA) Radiology Informatics Committee (RIC) and American College of Radiology (ACR) Informatics Committee as well as serving as informatics consultant to the RSNA. He has served as course director and/or faculty for over 300 courses for the RSNA, ACR, and for the Society for Imaging Informatics in Medicine (SIIM) in radiology informatics. In 2016, Dr. Chang was awarded the Gold Medal by the RSNA “for having revolutionized the practice of radiology through his expertise in the field of imaging informatics.”


DISCLOSURE OF CONFLICTS OF INTEREST
International Center for Postgraduate Medical Education (ICPME) requires instructors, planners, managers, and other individuals who are in a position to control the content of this activity to disclose any real or apparent conflict of interest (COI) they may have as related to the content of this activity. All identified COI are thoroughly vetted and resolved according to ICPME policy. The existence or absence of COI for everyone in a position to control content will be disclosed to participants prior to the start of each activity.

Paul Chang, MD, has received consulting fees from Bayer HealthCare Pharmaceuticals, AIDoc, and McCoy, and McCoy and has performed contracted research for Philips Healthcare.

The following planners and managers have reported NO financial relationships or relationships to products or devices they or their spouse/life partner have with commercial interests related to the content of this CME activity:

Sharon Cancino
Linda McLean, MS
Victoria Phoenix, BS


DISCLOSURE OF UNLABELED USE
This educational activity may contain discussion of published and/or investigational uses of products and devices that are not indicated by the FDA. The planners of this activity do not recommend the use of any products or devices outside of the labeled indications.  

The opinions expressed in the educational activity are those of the faculty and do not necessarily represent the views of the planners. Please refer to the official prescribing information for each product for discussion of approved indications, contraindications, and warnings.


PROVIDED BY

This activity is supported by an independent educational grant from
Bayer HealthCare Pharmaceuticals


Disclaimer
Participants have an implied responsibility to use the newly acquired information to enhance patient outcomes and their own professional development. The information presented in this activity is not meant to serve as a guideline for patient management. Any procedures, medications, or other courses of diagnosis or treatment discussed or suggested in this activity should not be used by clinicians without evaluation of their patient’s conditions and possible contraindications on dangers in use, review of any applicable manufacturer’s product information, and comparison with recommendations of other authorities.