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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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

AI triages pneumothorax patients with differentiated diagnoses

A commercially available AI package has proven adept at distinguishing between two closely similar but unequally urgent conditions on chest X-rays.

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AI specialists ink deal with Pfizer to target cardiac amyloidosis

Cardiac amyloidosis can be especially challenging to identify and diagnose, making it a perfect target for advanced AI models.

RadNet subsidiary gets green light for breast density AI

The FDA has cleared software that automatically assesses density of breast tissue on mammography.

Lack of transparency in AI research limits reproducibility, renders work 'worthless'

A recent analysis found that a significant amount of studies do not provide information pertaining to their raw data, source code or model. As a result, up to 97% of these studies do not produce systems that are fit to be used in real-world clinical scenarios. 

An example of artificial intelligence (AI) automated detection of a intracranial hemorrhage (ICH) in. a CT scan used to send alerts to the stroke acute care team before a radiologist even sees the exam. Example shown by TeraRecon at RSNA 2022.

VIDEO: Radiology AI aids acute care and other departments

Sanjay Parekh, PhD, senior market analyst with Signify Research, explains how some radiology AI is being adopted outside of radiology departments to improve care.

Juan Granada discusses new cardiac technology AHA22.

VIDEO: 2 key technologies to rapidly advance cardiovascular care

Juan Granada, MD, president and CEO of the Cardiovascular Research Foundation (CRF), shares his thoughts on two key areas of technology advancements in cardiovascular imaging and neuro-interventional care for stroke.

FDA move signals a forthcoming increase in the use of virtual and augmented reality devices in radiology

The applications for VR/AR devices are wide-ranging, and could be particularly beneficial in underserved areas where patients have less access to care and clinicians have fewer opportunities to train.

Example of AI automated detection and highlighting of critical lung findings on a chest X-ray for a possible lung cancer nodule and fibrosis. Example shown by AI vendor Lunit.

VIDEO: Radiology AI trends at RSNA 2022

Sanjay Parekh, PhD, senior market analyst with Signify Research, discusses trends in radiology AI seen on the expo floor and in sessions at RSNA 2022.

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