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 helps pathologists diagnose difficult breast cancer cases

A new machine learning system created by UCLA researchers may help doctors classify breast cancers that are notoriously difficult to diagnose, according to an Aug. 9 study published in JAMA Network Open.

August 9, 2019

Machine learning can avoid unnecessary CT use in PE patients

A neural network model can scour electronic medical record (EMR) data and determine if a patient has imaging-specific pulmonary embolism (PE)—a potential remedy for unnecessary CT imaging, reported authors of a multicenter study published in JAMA Network Open.

August 8, 2019

Ultrasound-guided IV placement improves first-attempt success in children

Physicians who used ultrasound to guide the placement of intravenous (IV) lines in young patients had better first-attempt success rates than those who used traditional methods, according to a study published in the July issue of Annals of Emergency Medicine.

August 6, 2019

A survey conducted by the Ann and Robert H. Lurie Children's Hospital of Chicago found more than 75% of parents are generally receptive to the use of artificial intelligence (AI) tools in the management of children with respiratory illnesses in the emergency department (ED). However, some demographic subgroups, including non-Hispanic black and younger age parents, had greater reservations about the use of these technologies.

Deep learning triages mammograms, reduces radiologists' workload nearly 20%

Deep learning can be used to triage cancer-free mammograms and improve the efficiency of radiologists, according to an Aug. 6 feasibility study published in Radiology.

August 6, 2019

Prepare for more AI at RSNA 2019

The 2019 Radiological Society of North America (RSNA) annual meeting in Chicago is expanding its artificial intelligence offerings.

July 31, 2019

Neuroimaging important for evaluating Zika-exposed infants

MRI and CT scans of infants exposed to the Zika virus in the womb revealed a range of brain abnormalities, reported authors of a recent study published in JAMA Network Open. The findings place neuroimaging as an important step in evaluating such patients.

July 31, 2019

AI boosts accuracy of DBT, slashes radiologists’ reading times

Utilizing an AI system for digital breast tomosynthesis (DBT) can improve radiologists’ accuracy while dramatically reducing reading times, according to a new study published in Radiology: Artificial Intelligence.

July 31, 2019

Deep learning reads x-rays to prevent mispositioned feeding tubes

A deep learning platform can accurately distinguish critical from non-critical feeding tube placement on radiographs, according to a recent study published in the Journal of Digital Imaging.

July 26, 2019

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.