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.

AI automation could 'revolutionize' Crohn's Disease severity CT assessments
Machine learning models could help create a more standardized, reproducible and efficient way of grading Crohn’s disease severity in the small bowel based on CT imaging.
New publicly available deep learning model for CT organ segmentation in children shows promise
The model was developed and validated specifically for liver, spleen and pancreas segmentation, and outperformed a publicly available segmentation model already in use.
New research offers reminder of why ChatGPT should not be used for second opinions
Although these tools have proven themselves valuable in numerous settings, they must be used with caution, especially by patients and nonradiologist providers who may be seeking clarification on imaging reports.
GPT-4 confidently struggles on radiology exam
Image-based questions were significantly more challenging for the large language model to answer, despite the latest version now being capable of accepting image prompts.
AI improves CT assessments, boosts care for real-world heart patients
Automated AI-generated measurements combined with annotated CT images can improve treatment planning and help referring physicians and patients better understand their disease, explained Sarah Jane Rinehart, MD, director of cardiac imaging with Charleston Area Medical Center.

AI turns low-quality brain scans into ‘super resolution’ MRIs

Experts believe their approach will allow specialists to pinpoint brain-related pathologies—such as physical injuries, cancer or language disorders, among other things—with improved accuracy.

January 17, 2020

NIH awards $725K grant to detect neonatal brain injury using photoacoustic imaging

Kamran Avanaki, PhD, will use the money to continue developing a point-of-care, 3D neonatal imaging system.

January 16, 2020

PET imaging reveals weakened brain connections in schizophrenia patients

The findings may offer new targets for researching and treating patients with the condition.

January 14, 2020

Researchers create first 3D image of heart RNA structure

Scientists from New Mexico-based Los Alamos National Laboratory believe their work could pave the way for new strategies in regenerative medicine for heart conditions.

January 10, 2020

‘The AI is simply outstanding’: Algorithm spots prostate cancer similarly to expert pathologists

AI trained and tested on more than 8,000 biopsies was nearly perfect at spotting differences in samples with or without cancer.

January 9, 2020

Radial 3D approach improves quality of head and neck images

Achieving a quality MRI of a patient’s head is difficult, but a radial gradient-recalled echo imaging protocol is a step in the right direction, researchers wrote Jan. 8 in the American Journal of Roentgenology.

January 9, 2020

Machine learning screens for obstructive CAD, reduces unnecessary imaging

By combining AI with coronary artery calcium scoring and other cardiac measurements, the team would have prevented 73 unnecessary scans.

January 8, 2020

System combining laser imaging, AI may make cancer operations safer and more effective

The new approach can diagnose brain tumors similarly to humans, but in a fraction of the time.

January 6, 2020

Around the web

Cardiovascular Business

AI improves CT assessments, boosts care for real-world heart patients

Automated AI-generated measurements combined with annotated CT images can improve treatment planning and help referring physicians and patients better understand their disease, explained Sarah Jane Rinehart, MD, director of cardiac imaging with Charleston Area Medical Center.

Cardiovascular Business

AI predicts cardiovascular risk during CT scans—no invasive tests or contrast required

Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans.

Radiology Business

Google teams with Bayer to address radiologist burnout via AI

"Gen AI can help tackle repetitive tasks and provide insights into massive datasets, saving valuable time," Thomas Kurian, CEO of Google Cloud, said Tuesday.