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.

Common features of breast cancers missed by AI
Imaging experts advocate for “meticulous assessments” of cases that include luminal cancer, dense breasts, and lesions located outside the mammary zone, among other factors.
Why do AI-savvy consumers harbor skepticism toward medical AI?
Patients who frequently use AI tend to readily trust AI-assisted diagnoses made by their physicians. Counterintuitively, however, those who would rank themselves among the very best-informed about AI tend to mistrust such diagnoses.
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.

Common features of breast cancers missed by AI

Imaging experts advocate for “meticulous assessments” of cases that include luminal cancer, dense breasts, and lesions located outside the mammary zone, among other factors.

Why do AI-savvy consumers harbor skepticism toward medical AI?

Patients who frequently use AI tend to readily trust AI-assisted diagnoses made by their physicians. Counterintuitively, however, those who would rank themselves among the very best-informed about AI tend to mistrust such diagnoses.

AI algorithm spots AAA on abdominal CT scan

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.

artificial intelligence AI in healthcare

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.

Exo Iris AI inks another FDA clearance for pleural effusion

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.

referral form referrer paperwork request radiology report

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.

AI model gets helpful heart assessments out of nongated CT scans

"Using AI for tasks like CAC detection can help shift medicine from a reactive approach to the proactive prevention of disease," one researcher said.