Health IT

Healthcare information (HIT) systems are designed to connect all the elements together for patient data, reports, medical imaging, billing, electronic medical record (EMR), hospital information system (HIS), PACS, cardiology information systems (CVIS), enterprise image systems, artificial intelligence (AI) applications, analytics, patient monitors, remote monitoring systems, inventory management, the hospital internet of things (IOT), cloud or onsite archive/storage, and cybersecurity.

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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.
 

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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