Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks. 

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AI research published without code, data, documentation interesting to readers but unhelpful to science: RSNA pubs review

Over the five-year period ending last December 31, only a third of 218 scientific articles on AI in four popular radiology journals shared the researchers’ code. 

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. 

Nuance and Covera join forces to improve radiology quality ‘at scale’

PowerScribe purveyor Nuance is partnering on widescale care improvement with a healthcare AI startup that made its name showing Walmart where, and where not, to send its employees for high-accuracy radiology.

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.

New national board forms, opens training course in radiological AI

An educational outfit has sprung up to equip nonphysicians working in radiology—chiefly administrators, business managers and technologists—with radiologist-level fluency in AI.

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