A post-game roundup by ITN Contributing Editor Greg Freiherr and ITN Editor Dave Fornell on the key trend of artificial intelligence seen on the show floor at the Radiological Society of North America (RSNA) 2017 meeting.

For more on artificial intelligence at RSNA 2017, watch the VIDEO "How Utilization of Artificial Intelligence Will Impact Radiology." For more trends in imaging, watch the VIDEO "Key Imaging Technology Trends at RSNA 2017." ITN also created an indepth VIDEO Technology Report — Artificial Intelligence at RSNA 2017  with interviews with numerous AI vendors.

J. Anthony Seibert, Ph.D., professor and chair of informatics, University of California Davis, describes the security vulnerabilities specific to radiology departments and how they can be combated at the 2017 Radiological Society of North America (RSNA) meeting.

Read the articles “Building A Cybersecurity Team in Radiology,” and “Healthcare's Growing Cybersecurity Threats.”

Adam Flanders, M.D., co-director, neuroradiology and vice-chair of informatics at Jefferson University Hospitals, Philadelphia, and chair of the RSNA Radiology Informatics Committee, discusses the impact of AI at RSNA 2017. Read the article “How Artificial Intelligence Will Change Medical Imaging.” Watch the related VIDEO “Machine Learning and the Future of Radiology,” an interview with Eliot Siegel, M.D.

Philips PerformanceBridge Practice dashboard, developed on the HealthSuite digital platform, offers at-a-glance insights into uptime, operations and performance across your imaging equipment. The dashboard also provides actionable recommendations, case studies and training alerts.

Randy Thompson, M.D., attending cardiologist, St. Luke’s Mid-America Heart Institute, Kansas City, explains protocols and what to consider when working with the newer generation CZT-SPECT camera systems for nuclear cardiology. He spoke during the 2017 American Society of Nuclear Cardiology (ASNC) Today technology update meeting. Watch the related VIDEO "PET vs. SPECT in Nuclear Cardiology and Recent Advances in Technology." Read the related articles "Managing Dose in PET and SPECT Myocardial Perfusion Imaging,"  and "Advances in Cardiac Nuclear Imaging."

Angelic McDonald, MSRS, CRA, FAHRA, regional director of imaging, Baylor Scott & White Health and the president-elect of AHRA, discusses the biggest challenges she and other radiology administrators face at the 2017 AHRA annual meeting in Anaheim. Read the related article "Two Key Issues Keeping Radiologists Up at Night in 2017."

Nationwide adoption of the Enterprise Imaging Health Record is inevitable as networks strive to improve patient safety and cost-to-diagnosis. But to optimize the EHR successfully requires thoughtful selection of systems, partners and governance processes. Enjoy this brief overview of what the successful selection should deliver.

Eliot Siegel, M.D., associate vice chair of diagnostic radiology and nuclear medicine, vice chair of information systems, University of Maryland, and chief of radiology, VA Maryland Healthcare System, discusses how machine learning (aka artificial intelligence or AI) is impacting radiology today and its role in the future.  

Read the article “How Artificial Intelligence Will Change Medical Imaging.” 

See examples of how AI can assess clinical images in the VIDEO “Examples of Artificial Intelligence in Medical Imaging Diagnostics.”

ITN also created an indepth VIDEO Technology Report — Artificial Intelligence at RSNA with interviews with numerous AI vendors.

James Whitfill, M.D., chief medical officer, Innovation Care Partners, and Christopher Roth, M.D., director of imaging informatics strategy, Duke Health, explain how the joint HIMSS-SIIM Enterprise Imaging Workgroup is working to enhance collaboration between various medical specialties to improve patient care.

Related Enterprise Imaging Content:

RSNA Technology Report 2017: Enterprise Imaging

VIDEO: Technology Report: Enterprise Imaging 2017

VIDEO: Building An Effective Enterprise Imaging Strategy

Five Steps for Better Diagnostic Image Management

VIDEO: Enterprise Imaging and the Digital Imaging Adoption Model

Enterprise Imaging to Account for 27 Percent of Imaging Market

VIDEO: How to Build An Enterprise Imaging System

Rami Doukky, M.D., system chair, Division of Cardiology, professor of medicine, Cook County Health and Hospitals System, Chicago, discusses the new CMS requirements for clinical decision support (CDS) appropriate use criteria (AUC) documentation in cardiac imaging starting on Jan. 1, 2018. He spoke at the 2017 American Society of Nuclear Cardiology (ASNC) Today meeting. Read the article "CMS to Require Appropriate Use Criteria Documentation for Medical Imaging Orders."

Alex Towbin, M.D., Neil D. Johnson Chair of Radiology Informatics at Cincinnati Children's Hospital, discusses the growing importance of translational and reproducible research in medical imaging informatics at SIIM 2017. 

Watch another VIDEO "Achieving Meaningful Patient Engagement" with Towbin at SIIM 2018.

Read the article "Essentials of Pediatric Imaging" with input from Towbin.

Mach7 offers an enterprise imaging platform built around neutrality. The platform is more than just a VNA. Eric Rice, chief technology officer, discusses the importance of workflow protocols.

ITN Editor Dave Fornell takes a tour of some of the most innovative new technology that was displayed on the expo floor at the 2017 Healthcare Information and Management Systems Society (HIMSS) annual meeting. The two most significant technology advances are discussed in Fornell's blog "Two Technologies That Offer a Paradigm Shift in Medicine at HIMSS 2017." This includes examples of artificial intelligence in Medical Imaging, radiology. 

Machine learning is now being commercialized in medical imaging products designed to help improve workflow efficiency and augment the clinical user, not replace them. Steve Holloway with the U.K.-based healthcare market intelligence firm Signify Research discussed the expanding roles of artificial intelligence in radiology at the 2017 HIMSS healthcare IT conference. He also offers examples of artificial Intelligence in medical imaging. Read the article “How Artificial Intelligence Will Change Medical Imaging.”

Watch this video to gain an understanding of the strategic purpose and clinical value of the enterprise imaging platform. Gain control of the custody of multispecialty images being acquired and managed inconsistently throughout your health system today. Learn how to convert image and information silos into collaborative assets to advance your transformation to value-based, connected care. 

Sectra provides industry-leading enterprise image management solutions comprising PACS for radiology, cardiology, and pathology, VNA and Cross Enterprise Workflow. Through 25 years of innovation and 1,700 installations, our experience in radiology has paved the way to deliver enterprise solutions that consolidate image handling and maintain workflow efficiency in the most image intense departments.

A value based care mindset calls for a value based care toolset. Conserus Workflow Intelligence helps radiologists demonstrate their value to the rest of the enterprise. It’s a rules engine that helps you get information and tasks to the right people at the right time. With this automated solution in place, you can match demand to supply and use your available resources and talents most effectively.

This solution is flexible, configurable and vendor-neutral. It consolidates a radiologist’s entire set of everyday tasks into a dynamic worklist and uses up to 99 levels of priority to determine which ones should be read first. The user interface is color coded so users can easily see which studies are the most urgent. It’s also dynamic, so a study’s priority level automatically adjusts and it moves to the top of the list as it becomes more urgent. It can even be configured to send alerts if studies aren’t read within your defined time period. It helps you close communication loops between departments and facilities

Whether your healthcare system involves one facility with a single PACS, or is a complex, distributed environment, Conserus Workflow Intelligence can help you make impactful, measurable improvements in areas like turnaround times and case prioritization. And once you’ve measured your progress, you can easily reconfigure your rules so your workflows can get more efficient.

Conserus Workflow Intelligence helps you improve quality and efficiency, all within your regular PACS workflow. To learn more about how it can help you improve communication and turnaround times in your health system, watch this brief video. 

A value based care mindset calls for a value based care toolset. Conserus Workflow Intelligence helps radiologists demonstrate their value to the rest of the enterprise. It’s a rules engine that helps you get information and tasks to the right people at the right time. With this automated solution in place, you can match demand to supply and use your available resources and talents most effectively.

This solution is flexible, configurable and vendor-neutral. It consolidates a radiologist’s entire set of everyday tasks into a dynamic worklist and uses up to 99 levels of priority to determine which ones should be read first. The user interface is color coded so users can easily see which studies are the most urgent. It’s also dynamic, so a study’s priority level automatically adjusts and it moves to the top of the list as it becomes more urgent. It can even be configured to send alerts if studies aren’t read within your defined time period. It helps you close communication loops between departments and facilities

Whether your healthcare system involves one facility with a single PACS, or is a complex, distributed environment, Conserus Workflow Intelligence can help you make impactful, measurable improvements in areas like turnaround times and case prioritization. And once you’ve measured your progress, you can easily reconfigure your rules so your workflows can get more efficient.

Conserus Workflow Intelligence helps you improve quality and efficiency, all within your regular PACS workflow. To learn more about how it can help you improve communication and turnaround times in your health system, watch this brief video. 

Kim Garriott, principal consultant for Logicalis Healthcare Solutions, explains the concept of value-based imaging and how it fits into healthcare reforms at RSNA 2016. Watch the related VIDEO "Value–based Imaging,” an interview with Daniel Berman, M.D., FACC, chief of Cardiac Imaging and Nuclear Cardiology, professor of imaging, Cedars-Sinai Medical Center. 
 

ITN and DAIC Editor Dave Fornell takes a tour of some of the most innovative new technologies being displayed on the expo floor at the Radiological Society of North America (RSNA) 2016 meeting. For key take away trends at RSNA, watch the video "Key Trends, New Technology at RSNA 2016."

At RSNA 2016, the key buzzwords were “deep learning,” “machine learning” and “artificial intelligence.” Vendors and major academic centers are developing a wide array of artificial intelligence neural networks to aid radiologists in clinical diagnosis and clinical decision support. In the future, AI may also be able to help train radiologists on both normal and abnormal presentations of various organs and body systems so as to more easily identify related disease states and conditions. The following video offers two examples of how the IBM Watson system examines imaging studies.

The first case seen here demonstrates how Watson can arrive at a differential diagnosis of an aortic dissection by analyzing an abdominal computed tomography (CT) scan. The second case involves the discovery of a fibroadenoma of the breast from Watson’s analysis of a mammogram.

Watson first analyzes the text of the radiology report, identifying and pulling out key words or phrases that may indicate the potential diagnosis. It then examines the CT scan to locate relevant visible anatomic structures such as the heart, aorta and pulmonary artery. Each structure is examined for anomalies, which identifies a possible aortic dissection; the dissection is displayed within the context of the entire 3-D CT scan. Finally, Watson applies its existing clinical knowledge to the findings from the CT scan and the radiology report, establishing pathways to numerous possible conclusions until arriving at the right one.

See examples of real products using AI at RSNA 2017 in the VIDEO "Examples of How Artificial Intelligence Will Improve Medical Imaging."  ITN also created an in-depth VIDEO: Technology Report — Artificial Intelligence at RSNA 2017,  with interviews with numerous AI vendors.

Watch the VIDEO: “Development of Artificial Intelligence to Aid Radiology,” an interview with Mark Michalski, M.D., director of the Center for Clinical Data Science at Massachusetts General Hospital, explaining the basis of artificial intelligence in radiology.