Imaging Informatics

Imaging informatics (also known as radiology informatics, a component of wider medical or healthcare informatics) includes systems to transfer images and radiology data between radiologists, referring physicians, patients and the entire enterprise. This includes picture archiving and communication systems (PACS), wider enterprise image systems, radiology information. systems (RIS), connections to share data with the electronic medical record (EMR), and software to enable advanced visualization, reporting, artificial intelligence (AI) applications, analytics, exam ordering, clinical decision support, dictation, and remote image sharing and viewing systems.

Example of a radiology diagnostic aid artificial intelligence (AI) algorithm with Lunit's mammography cancer lesion detection system.

VIDEO: Segmenting the Radiology Artificial Intelligence Market by Function

Keith J. Dreyer, DO, American College of Radiology (ACR) Data Science Institute chief science officer, breaks down radiology AI down into 4 areas and discusses where these areas stand with regulatory approval.

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Addressing 'model drift' to recover AI performance before it leads to report errors

“Although regularly assessing and updating these models is necessary to ensure accurate performance, there is no standard approach to addressing model drift.” 

Example of an artificial intelligence (AI) app store on the Sectra website, where Sectra PACS users can select the AI algorithms they want that are already integrated into the Sectra System. Other vendors have followed a similar approach to AI developed by many smaller vendors they partner with.

VIDEO: Development of AI app stores to enable easier access

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains how radiology vendors have developed AI app stores to make it easier to access new FDA cleared AI algorithms.
 

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains artificial intelligence (AI) for radiology. Dreyer also holds the positions of vice chairman of radiology at Massachusetts General Hospital, chief data science and information officer for the departments of radiology for both Massachusetts General Hospital and Brigham and Women's Hospital.

VIDEO: Where will radiology AI be in 5 years?

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains 5 developments to watch for in radiology artificial intelligence (AI).

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How do radiologists really feel about adopting AI? New data offer insight

Up to 60% of radiologists have intentions of adopting artificial intelligence tools into clinical practice in the near future. 

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EHR tracking system significantly improves diagnostic timelines for liver cancer patients

Implementing an EHR cancer tracking system to review radiology reports for abnormal findings resulted in patients at one Veterans Affairs Hospital receiving their cancer diagnosis and treatment months earlier than those who were imaged before the system was put into place.

radiology reporting EHR health record CDS AUC

EHR-based solutions to the iodinated contrast shortage reduce usage by 12%

This week in AJR, experts from a large, multisite health system detailed their efforts to preserve contrast supplies by implementing electronic health record (EHR) order entry-based interventions.

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Why do so few patients utilize online access to radiology results?

Of 139,000 individuals enrolled in an online patient portal, just 27% viewed their radiology reports.