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RSNA Dives Deep on Artificial Intelligence for Radiologists

The commercial market for artificial intelligence (AI) in healthcare is projected to grow at a compound annual growth rate of over 68 percent through the 2018-2022 timespan, according to industry researcher Frost & Sullivan. Driving the expansion: a tangible shift from innovation to adoption of AI among radiologists, because new tools are proving useful in the field.

For instance, the University of Utah Health is putting AI to work compiling patients’ prior scans, as opposed to physicians having to manually search archived images. And at Capital Health Hospitals, AI-based clinical software detects intracranial hemorrhages in CT scans and flags them for immediate attention.

New Platform for AI Research

Amidst this fast-developing setting, the Radiological Society of North America (RSNA) recently launched an online journal, Radiology: Artificial Intelligence, which highlights emerging AI applications across multiple imaging disciplines.

“AI and radiology do not exist in isolation,” explains the publication’s editor, Charles Kahn, MD. “[These] technologies will help us care for our patients more effectively and humanely. Our goal is not to replace, but rather to extend our human abilities to provide medical care — and to improve the lives of those we are privileged to serve.”

At the journal’s core will be validated scientific research papers that show AI’s impact in extracting information, diagnosing and managing diseases, streamlining radiology workflow and improving healthcare outcomes. Expect coverage of image segmentation and reconstruction, automated detection of abnormalities, diagnostic reasoning, natural language processing, clinical workflow analysis, and radiogenomics, as well as novel applications and innovative applications.

The debut issue, published January 30, includes analysis of automated fracture detection and localization on wrist radiographs, and classification of elbow fractures using a “deep learning” approach that emulates radiologist decision-making. A special report looks at how AI provides standardization, consistency, and dependability in support of human radiologists. An opinion piece peers over the horizon at “augmented radiology,” a practice in which technology will amplify human insight, particularly in medical education and training.

Toward Full AI Integration

As pointed out in NetDirector’s blog post “Artificial Intelligence Set to Soar in Healthcare,” AI’s future success rides on use cases where the technology not only helps to improve clinical outcomes but also delivers a clear return on investment. In doing so, it needs to be fully integrated into radiology departments’ user interfaces and workflows.

Areas to watch include breast and lung imaging for cancer discovery, neurological imaging for stroke detection and non-invasive imaging for diagnosis of coronary artery disease. The technology works in the background to support radiologists’ knowledge and efficiency while offering readily accessible tools for specific purposes as needed.

Undoubtedly, an ongoing challenge will be assimilating health data across diverse platforms and connecting multiple data sources. NetDirector’s cloud-based HealthData Exchange platform ensures strong integration for fast-rising AI applications, bolstered by an existing footprint in radiology and imaging centers.

To find out more about HealthData Exchange and how it could help leverage AI applications, please contact us or request a free demo.

Artificial Intelligence Set to Soar in Healthcare

The market for artificial intelligence (AI)-based medical image analysis software will grow exponentially over the next several years, from the current level of approximately $400 million to more than $2 billion in 2023, according to a recent report from Signify Research. Product development pace is at an all-time high, driven in part by the improved performance of AI algorithms and rapid advancements in computing, storage, and networking capabilities.

Nonetheless, the promising outlook hinges on algorithm developers identifying use-cases where “AI can be shown to improve clinical outcomes and deliver a clear return on investment for healthcare providers,” writes analyst Simon Harris, author of the report. “Moreover, the technology needs to be fully integrated in the existing user interfaces and workflows found in radiology departments, both working in the background to augment radiologists’ knowledge and efficiency, and [being] readily accessible when specific tools are needed.”

Areas to watch include breast and lung imaging for cancer detection, neurological imaging for stroke detection, and non-invasive imaging for the diagnosis of coronary artery disease, according to Signify. If things go as predicted, patients would benefit from personalized treatment made possible by higher accuracy in diagnostic imaging, and radiology departments would be better equipped to handle increasing workloads.

Practical Applications of AI

Many AI algorithms in development for radiology address detection of abnormal structures in diagnostic images and can be used in modalities ranging from CT scans to X-rays.

The technology automates the handling of data-intensive studies such as mammograms, which are transitioning from 2D to 3D imaging, notes Matt Dewey, CIO of Wake Radiology in the Raleigh-Durham, N.C., area. “We go from a study that used to be 64 megabytes for a normal, standard study to about 2 gigabytes, so it just takes the radiologist much more time to go through,” Dewey explains. “If we can find a way that a computer looks through it, it should make a difference [by highlighting] things for the radiologist.”

Additionally, AI could help by analyzing data from non-radiology sources such as lab test results and patient-specific files from electronic health record systems. AI’s role would be to extract key pieces of information for each case, says Dewey.

Elsewhere in real-world AI applications:

  • Mayo Clinic is conducting molecular sequencing and analysis for 1,000 patient participants in immunotherapy studies for various cancer types. The results will help shape customized treatment options.
  • Cleveland Clinic has integrated Microsoft’s Cortana AI digital assistant into a command center that monitors 100 beds in six ICUs on overnight shifts. The focus is on identifying patients at high risk for cardiac arrest.
  • Massachusetts General Hospital has installed a “deep learning” supercomputer to tap a database of 10 billion images for applications in radiology and pathology.
  • Johns Hopkins uses predictive analytics to support more efficient operational flow. Among the targets are faster ambulance dispatches, streamlined bed assignments in the emergency department and more patient discharges before noon each day.
  • UCLA Medical Center is testing an AI-driven chatbot that communicates with referring clinicians and provides evidence-based answers to frequently asked questions.

Integration Will Fuel AI’s ‘Engine for Growth’

From a broad perspective across healthcare, AI applications constitute a “self-running engine for growth,” with the potential to create $150 billion in annual savings by 2026, according to consulting firm Accenture.

In pursuit of those projected gains, the technology challenge will be integrating health data across platforms and connecting various data sources.

NetDirector’s cloud-based HealthData Exchange already has a footprint in radiology and imaging centers, enabling them to reduce integration costs and facilitate improved workflows and communications with the extended provider community.

If your organization is investigating or underway with AI-based initiatives, consider how HealthData Exchange can ensure strong integration moving forward across multiple systems and provider networks.

To find out more about NetDirector’s HealthData Exchange platform, please contact us or request a free demo.