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.

Healthcare AI today: Bipartisan AI policy guidance, AI claims denials, LLM brain rot, more
Federal lawmakers have a fresh resource to keep them up on healthcare AI that falls outside of formal FDA oversight. It comes in the form of a new report from the nonprofit Bipartisan Policy Center.
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.

AI helps radiologists detect more breast cancers

Artificial intelligence (AI) support systems for reading mammograms can improve a radiologist’s ability to detect cancers, according to a new study published in Radiology. Using the system, the authors added, does not lengthen the overall reading time.

3D MRI comparable to 2D for diagnosing meniscal knee injuries

Three-dimensional (3D) MRI is similar to 2D for diagnosing meniscus knee injuries, but may be able to cut down on image acquisition time, reported authors of a Nov. 20 study published in Radiology.

3D images from total-body scanner to be presented at RSNA 2018

Images from the world’s first whole-body MRI scanner are set to be presented at this year’s 2018 RSNA Annual Meeting in Chicago, according to a University of California, Davis statement.

Lung cancers efficiently identified, characterized with novel AI approach

Researchers at the State University of New York at Stony Brook have demonstrated a deep-learning algorithm that can quickly diagnose early-stage lung cancer on CT scans by combining computerized self-trained tumor identification with engineered identification of specific tumor features.

Predictive model may reduce overtreatment of ground glass nodules

A model based on radiomic features extracted from CT scans can help predict which ground glass nodule (GGN) cases require surgery and may reduce overtreatment, according to researchers at the Affiliated Suzhou Hospital of Nanjing Medical University in Suzhou, China.

Philips launches IntelliSpace Discovery* Research platform at RSNA to support the development and deployment of Artificial Intelligence assets in radiology

Royal Philips

Machine learning method helps radiologists diagnose uterine cancer

A machine learning algorithm based on perfusion-weighted MRI accurately differentiated between benign and malignant tumors in the uterus, according to researchers at Tehran University of Medical Sciences (TUMS) in Iran.

4 areas where AI is having its biggest impact on breast imaging

Artificial intelligence (AI) technologies are advancing at a rapid rate and starting to make a direct impact on breast imaging. There is still a lot of work to be done, however, before AI can truly be trusted with making decisions that may impact a patient’s survival, according to a new commentary published in the American Journal of Roentgenology.

Artificial Intelligence

Healthcare AI today: Bipartisan AI policy guidance, AI claims denials, LLM brain rot, more

Common features of breast cancers missed by AI

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

AAA detection gets boost from commercially available AI algorithm

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