Enterprise Imaging

Enterprise imaging brings together all imaging exams, patient data and reports from across a healthcare system into one location to aid efficiency and economy of scale for data storage. This enables immediate access to images and reports any clinical user of the electronic medical record (EMR) across a healthcare system, regardless of location. Enterprise imaging (EI) systems replace the former system of using a variety of disparate, siloed picture archiving and communication systems (PACS), radiology information systems (RIS), and a variety of separate, dedicated workstations and logins to view or post-process different imaging modalities. Often these siloed systems cannot interoperate and cannot easily be connected. Web-based EI systems are becoming the standard across most healthcare systems to incorporate not only radiology, but also cardiology (CVIS), pathology and dozens of other departments to centralize all patient data into one cloud-based data storage and data management system.

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VIDEO: Where are we with AI adoption in radiology?

Bibb Allen, MD, FACR, chief medical officer of the American College of Radiology (ACR) Data Science Institute, discusses multiple factors involved in the adoption rate of artificial intelligence in radiology.
 

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, explains considerations for healthcare system information technology (IT) management teams on the implementation of artificial intelligence (AI). He also discusses ideally how AI should be integrated into medical IT systems, and some of the issues AI presents in the complex environment of real-world patient care." #AI #HIMSS

VIDEO: How hospital IT teams should manage implementation of AI algorithms

Julius Bogdan, vice president and general manager of the HIMSS Digital Health Advisory Team for North America, explains considerations for healthcare IT teams on the implementation of artificial intelligence (AI).

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Concerns raised over how hospitals can validate radiology AI algorithms

As artificial intelligence (AI) adoption expands in radiology, there is growing concern that AI algorithms need to undergo quality assurance (QA) reviews.

Bibb Allen, MD, FACR, chief medical officer of the American College of Radiology (ACR) Data Science Institute, and former ACR president, explains how hospitals or radiology departments can conduct quality assurance (QA) assessments on artificial intelligence (AI) algorithms they adopt to ensure they are accurate. The ACR established the Assess-AI Registry and AI-Lab to help with validating and tracking AI QA for FDA-cleared algorithms.

VIDEO: Validation monitoring for radiology AI to ensure accuracy

Bibb Allen, MD, FACR, Chief Medical Officer of the American College of Radiology (ACR) Data Science Institute, and former ACR president, explains how hospitals or radiology departments can conduct quality assurance assessments on artificial intelligence algorithms they adopt to ensure they are accurate. 

An overview of artificial intelligence (AI) in radiology with Keith Dreyer with the ACR. Images shows a COVID-19 lung CT scan reconstruction from Siemens Healthineers. #AI #radAI #ACR

VIDEO: Overview of radiology AI by Keith Dreyer

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains the state of AI in radiology in 2022. 

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.
 

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