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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ZEISS introduces machine learning capability for microscopy

First ZEISS ZEN Intellesis solution enables segmentation of correlative microscopy datasets.

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MRI study finds prenatal exposure to certain antidepressants may alter brain development

Researchers from New York and California recently used MRI to determine prenatal exposure to commonly used antidepressants in pregnant women may be associated with impacted fetal brain development—particularly in areas crucial to emotions.

MRI of tumor surface regularity may aid surgery, predict survival in glioblastoma patients

A team of international researchers published a study in Radiology that found surface regularity taken from high-resolution contrast-enhanced pretreatment volumetric T1-weighted MRIs to be an accurate predictor of survival in patients with specific malignant brain or spine tumors.

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Deep learning improves radiologist workflow, efficiency determining musculoskeletal MRI protocol

Deep learning and artificial intelligence (AI) are often associated with identifying nodules and classifying images, but a recent study found convolutional neural networks (CNNs) can be utilized in radiology workflows to determine musculoskeletal MRI protocols.

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Microscopic imaging finds new organ—dubbed the 'highway of moving fluid'

Calling the discovery of a new organ in the human body surprising is a bit of an understatement, but that's what a study published in Scientific Reports claims.

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AI digital pathology may help guide cancer therapies

A team of Stony Brook University-led researchers in New York created a method using deep learning digital pathology to map cancerous immune cell patters that may help guide new cancer therapies.

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Ohio State AI algorithm can search for 4 types of brain abnormalities in 6 seconds

Ohio State University researchers have developed an artificial intelligence (AI) algorithm able to analyze a single brain CT scan in just six seconds, according to an article published online March 28 by the Lantern.

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A radiologist’s guide to deep learning

Eight members of the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning describe a radiologist-friendly overview examining past, present and future applications and how the field might benefit from embracing deep learning.

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Around the web

Radiology Business
The biggest obstacles facing radiology business managers in 2025

RBMA President Peter Moffatt discusses declining reimbursement rates, recruiting challenges and the role of artificial intelligence in transforming the industry.

Cardiovascular Business
New cardiac prevention paradigm explored in TRANSFORM trial using AI and CCTA

Deepak Bhatt, MD, director of the Mount Sinai Fuster Heart Hospital and principal investigator of the TRANSFORM trial, explains an emerging technique for cardiac screening: combining coronary CT angiography with artificial intelligence for plaque analysis to create an approach similar to mammography.

Cardiovascular Business
Cardiology practices pay $17.7M to settle fraud allegations brought on by whistleblowers

A total of 16 cardiology practices from 12 states settled with the DOJ to resolve allegations they overbilled Medicare for imaging agents used to diagnose cardiovascular disease.