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.

AAA detection gets boost from commercially available AI algorithm
The opportunistic screening helps identify patients in need of monitoring and/or intervention without disrupting workflows.
Healthcare AI today: Senate arguments, aging infrastructure, ‘elastic’ jobs, more
States’ rights or federal power? Senate Republicans are of two minds on AI regulation in the One Big Beautiful Bill.
That’s no mere hospital—it’s a ‘complex adaptive system.’ Use GenAI accordingly.
From the perspective of management science, healthcare is a complex adaptive system marked by intricate feedback loops and overlapping interdependencies. As such, the sector demands caution by those introducing large-language AI into its tangled webs.
First AI-enabled POCUS app for detecting pleural effusion and atelectasis earns FDA clearance
The apps are embedded into the Exo Iris transducer, which can be plugged directly into a smartphone, utilized anywhere and without the internet.
Startup seeking to simplify medical imaging referrals raises $101M
Tennr uses an “enterprise orchestration engine” and series of specialized language models, trained on the nuances of processing documentation against payer criteria.

Optoacoustic imaging can noninvasively diagnose thyroid disorders

Pairing multispectral optoacoustic tomography with ultrasound provided biomarker information that may better diagnose patient's with autoimmune diseases and thyroid nodules.

AI helps radiology residents spot abnormal x-rays in the ED

The commercially available algorithm helped residents improve their sensitivity at spotting abnormal findings in chest x-rays.

Radiomics model beats radiologists at categorizing BI-RADS 4 lesions

Mammography does a good job detecting calcifications, but its specificity for distinguishing benign from malignant findings remains low.

AI detects tiny brain hemorrhages on CT scans, outperforms radiologists

“Given the large number of people who suffer from traumatic brain injury every day and are rushed to the emergency department, this has very big clinical importance," study authors said.

AI rivals radiologists in finding brain hemorrhages

A deep-learning algorithm can be as effective—or more effective—than radiologists in finding intercranial hemorrhages on CT scans.

Mammography-based radiomics helps categorize BI-RADS 4 lesions

BI-RADS 4 lesions are considered among the most difficult and suspicious category of breast lesions.

AI solution for Canon Medical’s CT systems receives FDA approval

Canon Medical Systems USA announced Monday, Oct. 21, that the Advanced Intelligent Clear-IQ Engine (AiCE) for its ultra-high resolution CT scanner has gained FDA approval.

4 ways imaging informatics is central to AI in radiology

“With the ability to understand each of the different domains and translate between the experts in these domains, imaging informaticists are now essential players in the development, evaluation and deployment of AI in the clinical environment.”

Artificial Intelligence

AAA detection gets boost from commercially available AI algorithm

Healthcare AI today: Senate arguments, aging infrastructure, ‘elastic’ jobs, more

That’s no mere hospital—it’s a ‘complex adaptive system.’ Use GenAI accordingly.

First AI-enabled POCUS app for detecting pleural effusion and atelectasis earns FDA clearance

Startup seeking to simplify medical imaging referrals raises $101M